Adaptive receiver

ABSTRACT

A nonlinear control system and a speaker protection system are disclosed. In particular, a control system for adapting an audio output from a speaker in the proximity of an object is disclosed. The controller is configured to accept one or more input signals, and one or more estimated states produced by the model to produce one or more control signals. A speaker protection system and a quality control system are disclosed. More particularly, a system for clamping the input to a speaker dependent upon an estimate of the proximity, acoustic volume, and/or acoustic coupling of the speaker to a nearby object is disclosed.

TECHNICAL FIELD

The present disclosure is directed to digital control of receivers andprotection systems for implementation in audio signal processing. Thepresent disclosure is further directed towards adaptive audio correctionof handsets. The present disclosure is further directed towards systemsand methods for predicting the acoustic impedance of a handset coupledwith the head of a user.

BACKGROUND

Mobile technologies and consumer electronic devices (CED) continue toexpand in use and scope throughout the world. In parallel with continuedproliferation, there is rapid technical advance of device hardware andcomponents, leading to increased computing capability and incorporationof new peripherals onboard a device along with reductions in devicesize, power consumption, etc. Most devices, such as mobile phones,tablets, and laptops, include audio communication systems andparticularly one or more speakers to interact with and/or stream audiodata to a user.

Every device has an acoustic signature, meaning the audiblecharacteristics of a device dictated by its makeup and design thatinfluence the sound generated by the device or the way it interacts withsound. The acoustic signature may include a range of nonlinear aspects,which potentially depend on the design of the device, on the age of thedevice, the content of an associated stream (e.g., sound pressure level,spectrum, etc.), and/or the environment in which the device operates.The acoustic signature of the device may significantly influence theaudio experience of a user.

Improved acoustic performance may be achieved, generally with additionalcost, increased computational complexity, and/or increased componentsize. Such aspects are in conflict with the current design trend. Assuch, cost, computation, and size sensitive approaches to addressingnonlinear acoustic signatures of devices would be a welcome addition toa designer's toolbox.

Tuning is generally performed during design and validation of a newhandset. Tuning is performed by testing the new handset on a range ofear simulators, as well as head and torso simulators, and the tuningprocess is directed by a series of standard performance levels. Suchsimulators are static and rarely reflective of actual operatingconditions of the handset in the real world.

In real world conditions, the actual acoustic impedance between ahandset receiver and the ear of a subject may change dramatically basedon position, quality of the seal against the ear, physiology of the ear,hair coverage around the ear, clothing worn around the head of thesubject, jewelry worn on the ear of the subject, ear modifications,environmental conditions, and the like.

There is a need for improving perceived sound quality from a handset inreal world settings.

SUMMARY

One objective of this disclosure is to provide a control system for aspeaker.

Another objective is to provide a filter system for enhancing audiooutput from a consumer electronics device.

Yet another objective is to provide a control system for enhancing anaudio output from a speaker in vicinity with an object to the object.

Another objective is to provide improved sound quality to a subject whena handset receiver is coupled with the head of the subject.

Another objective is to enhance an audio output from a speaker in a loudenvironment, when the speaker is placed near to an ear of a subject.

Yet another objective is to provide a simplified and reliable speaker.

The above objectives are wholly or partially met by devices, systems,and methods according to the appended claims in accordance with thepresent disclosure. Features and aspects are set forth in the appendedclaims, in the following description, and in the annexed drawings inaccordance with the present disclosure.

According to a first aspect there is provided, a control system forrendering an audio stream on a speaker from one or more input signals,the control system including an estimator with one or more stateestimating models, each state estimating model configured to accept oneor more of the input signals, and/or one or more feedback signals, andto generate one or more estimated states therefrom, one or more of thestate estimating models configured to estimate one or more proximitystates, the proximity states related to an orientation, acoustic volume,and/or acoustic coupling of the speaker to a nearby object, and acontrol block including a control algorithm configured to accept one ormore of the input signals and/or delayed versions thereof, and theproximity states and/or one or more signals generated therefrom, and togenerate the audio stream therefrom.

In aspects, the proximity state estimating model may include an adaptivefeed forward model in accordance with the present disclosure. Inaspects, the proximity state may be reflective of an acoustic couplingbetween the speaker and the object, an acoustic leakage between thespeaker and the object, an acoustic load on the speaker, an acousticvolume between the speaker and the object, an alignment between thespeaker and the object, the proximity or distance of the speaker to theobject, a tightness of fit between the speaker and the object, a stateof cleanliness of the speaker, a combination thereof, or the like.

In aspects, the control system may be configured to perform therendering at a first rate, and the control system may include a feedbackalgorithm, the feedback algorithm coupled to a feedback sensorconfigured to generate at least a portion of the feedback signal, thefeedback algorithm configured to update the proximity state estimatingmodel, the proximity state estimating model updated at a substantiallyslow rate, compared with the first rate.

In aspects, the feedback sensor may be a microphone, a speaker impedancesensor, a current sensor, a voltage sensor, a coulomb counting sensor, apressure sensor, a humidity sensor, an infrared proximity sensor, athermal sensor, a colorimetric sensor, an imaging sensor, a combinationthereof, or the like.

In aspects, the feedback sensor may be a microphone, the microphonelocated in close proximity to both the speaker and the object. Inaspects, the microphone may be located substantially between the speakerand the object. Such a configuration may be advantageous to estimate theacoustic volume formed between the speaker and the object.

In aspects, the system may include a model for estimating an acousticpressure between the object and the speaker, estimating the acousticleakage, and/or acoustic volume between the object and the speaker, orthe like, in accordance with the present disclosure.

In aspects, the microphone and the speaker may be included in a handset,the handset including regions including a mouthpiece and an ear piece,the microphone and the speaker situated near the ear piece. By ear pieceis meant a region of the handset that is meant to be placed against anear of a subject during use. By mouthpiece is meant a region of thehandset that is meant to acoustically interface with the mouth of asubject during use. In aspects, the microphone may be locatedsubstantially far away from the speaker and/or the object.

In aspects, the microphone and the speaker may be included in a handset,the handset in accordance with the present disclosure, the microphonesituated near the mouthpiece, and the speaker situated near the earpiece.

In aspects, the speaker may be operated in a sensing mode, the coulombtransfer to/from, current to/from, and/or voltage across the speakerproviding the feedback signal or a portion thereof.

In aspects, the control system may be coupled to a diagnostic speaker(or the speaker may be operated as a diagnostic speaker), the controlsystem configured to render a diagnostic audio stream on the diagnosticspeaker, the feedback sensor configured to generate a feedback signalrelated to the diagnostic audio stream, the feedback algorithmconfigured to analyze the diagnostic audio stream and the feedbacksignal to calculate the proximity state.

In aspects, control system may include an excursion state estimatingmodel, configured to estimate an excursion state of the speaker, theexcursion state used to update the proximity state estimating model.

In aspects, the feedback signal may be related to a background noiselevel in the vicinity of the speaker and/or the object, the controlsystem configured to adjust the audiostream in accordance with one ormore characteristics of the background noise.

In aspects, the feedback signal may be related to a background noiselevel in the vicinity of the speaker, the changes in background noiselevel used in estimating one or more of the proximity states. The systemmay include a background noise level analyzing function, wherein theanalyzing function estimates one or more characteristics of thebackground noise level (e.g. amplitude, power level, spectrum, lowfrequency component, high frequency component, noise in an audio band,noise in a voice band, etc.) from the feedback signal, and sets acontrol parameter related to one or more of the characteristics.

In aspects, the control system, and the speaker may be included in ahandset, the handset in accordance with the present disclosure, thespeaker situated near the ear piece, the handset including one or moresensors to obtain one or more of the feedback signals, a first feedbacksignal relating to background noise in the vicinity of the speaker,between the speaker and the object, and/or in the vicinity of the earpiece, and a second feedback signal relating to background noise in thevicinity of the mouthpiece, and/or from a remote location, the controlsystem configured to measure one or more differences between the firstand second feedback signals, the differences used in the estimation ofone or more of the proximity states.

In aspects, the control system may be configured to coordinateproduction of the diagnostic signals, and capture of the feedbacksignals, such that the feedback signals are related to the diagnosticsignals.

According to aspects there is provided, a control system for renderingan audio stream on a speaker from one or more input signals, the controlsystem including one or more feedback sensors configured to generate oneor more feedback signals, each feedback signal influenced by a proximityof the speaker to a nearby object, a proximity estimating block coupledwith the feedback sensor(s), the proximity estimating block configuredto accept the feedback signal(s) and to generate a proximity model, anda control block including a control algorithm configured to accept oneor more of the input signals and/or delayed versions thereof, and theproximity model and/or a signal generated therefrom, and to generate theaudio stream therefrom.

In aspects, the proximity model may be reflective of an acousticcoupling between the speaker and the object, an acoustic leakage betweenthe speaker and the object, an acoustic volume between the speaker andthe object, an acoustic load on the speaker, an alignment between thespeaker and the object, the proximity or distance of the speaker to theobject, a tightness of fit between the speaker and the object, a stateof cleanliness of the speaker, a combination thereof, or the like. Inaspects, the system may be configured such that the rendering is updatedat a first rate and the proximity model updated at a substantiallyslower rate compared to the first rate.

In aspects, one or more of the feedback sensors may be a microphone, aspeaker impedance sensor, a current sensor, a voltage sensor, a coulombcounting sensor, a pressure sensor, a humidity sensor, an infraredproximity sensor, a thermal sensor, a colorimetric sensor, an imagingsensor, a combination thereof, or the like.

In aspects, one of the feedback sensors may be a microphone, themicrophone located in close proximity to both the speaker and theobject. In aspects, the control system, the microphone, and the speakerare included in a handset in accordance with the present disclosure, thehandset including regions including a mouthpiece, and an earpiece, themicrophone and the speaker situated in the vicinity of the earpiece.

In aspects, one of the feedback sensors may be a microphone, themicrophone located substantially far away from the speaker and/or theobject. In aspects, the control system, the microphone and the speakermay be included in a handset, with regions including a mouthpiece and anear piece, the microphone situated near the mouthpiece, and the speakersituated near the ear piece.

In aspects, the control system may be coupled to a diagnostic speaker,the control system configured to render a diagnostic audio stream on thediagnostic speaker, one or more of the feedback sensors configured togenerate at least a portion of a feedback signal related to thediagnostic audio stream, the feedback algorithm configured to analyzethe diagnostic audio stream and the feedback signal to calculate theproximity parameter.

In aspects, the diagnostic speaker may be located near the mouthpiece,and the one or more of the feedback sensors is located near themouthpiece and/or the earpiece.

In aspects, control system may include an excursion state estimatingmodel configured to estimate an excursion parameter, an excursion model,and/or state of the speaker, the excursion parameter, the excursionmodel, and/or state used to update the proximity model. In aspects, thecontrol system may be configured to throttle the audio stream based onthe proximity model or a signal generated therefrom.

In aspects, one of the feedback sensors may be an imaging sensor, theimaging sensor configured to capture one or more images of the object,the control system including an image processing algorithm, the imageprocessing algorithm configured to generate one or more of the feedbacksignals from the images, one or more of the feedback signals relating toalignment of the object to the speaker, proximity of the object to thespeaker, an anatomical characteristic of the object, one or more changesin the object, or a combination thereof. In aspects, the object maybe anear, a head, a face, a neck of a subject, or the like.

According to aspects there is provided, a handset including a speakerconfigured to render an audio stream for a user, a control system inaccordance with the present disclosure coupled to the speaker, thecontrol system configured to accept one or more input signals and toproduce the audio stream or signal related thereto.

In aspects, the handset may include a wireless connection to acloud-based database, the audio stream, proximity estimating model,object images, feedback signal, and/or one or more signals relatedthereto stored in the cloud-based database.

According to aspects there is provided, a method for rendering an audiostream on a speaker near an object including accepting one or more inputsignals including an audio stream, estimating a proximity state relatingto an orientation, distance, bias pressure, an acoustic volume, and/oracoustic coupling between the speaker and the object; and altering theaudio stream based upon the proximity state.

In aspects, the method may include estimating the proximity state with aplurality of proximity models, and determining which estimated speakerstates best represents proximity between the speaker and the object.

In aspects, the step of altering the audio stream may include limitingthe audio stream based upon the value of the estimated proximity state.

In aspects, the estimating may include measuring a signal from one ormore feedback sensors, wherein one or more of the sensors is amicrophone, a speaker impedance sensor, a current sensor, a voltagesensor, a coulomb counting sensor, a pressure sensor, a humidity sensor,an infrared proximity sensor, a thermal sensor, a colorimetric sensor,an imaging sensor, a combination thereof, or the like.

In aspects, the method may include measuring a feedback signal from thespeaker, and the estimating step is dependent at least in part upon thefeedback signal.

In aspects, the method may include calculating proximity estimates andspeaker output estimates from corresponding model pairs, comparing theoutput estimates from each model pair with a feedback signal from thespeaker and or feedback sensor to select the best model pair, andselecting the best estimated proximity state from the best model pair.

In aspects there is provided, a speaker control system for producing arendered audio stream from one or more input signals including anestimator including one or more state estimating models, each stateestimating model configured to accept one or more of the input signals,and to generate one or more estimated states therefrom; and a speakerprotection block configured to accept one or more of the input signalsand/or delayed versions thereof and the estimated states and/or signalsgenerated therefrom, and to produce an output signal from a combinationthereof.

In aspects, the speaker protection block may include a compressor, alimiter, a clipper, or the like in order to produce the output signal.One or more characteristics of the compressor/limiter/clipper (e.g.,gain, cutoff amplitude, threshold for compression, etc.) may bedependent upon the estimated states, and applied to the input signal.

In aspects, the system may include a selector in accordance with thepresent disclosure coupled to the estimator and the speaker protectionblock, configured to analyze one or more of the estimated states and/orstate estimating models, and to generate an estimating signal therefrom,the speaker protection block configured to use the estimating signal inthe production of the output signal.

In aspects, the selector may be configured to select the worst caseestimated state from the estimated states, the estimating signaldependent upon the worst case estimated state.

In aspects, the system may include a feedback block in accordance withthe present disclosure coupled to an associated speaker, the estimator,and/or the selector, configured to provide one or more feedback signalsfrom the speaker to the selector, the selector configured to use one ormore of the feedback signals in the generation of the estimating signal.

In aspects, the system may include a feedback block in accordance withthe present disclosure coupled to an associated speaker and/or driverconfigured to provide one or more feedback signals or signals generatedtherefrom to the system, a model bank including a group of models eachwith associated characteristics, and a selector coupled to the feedbackblock, the model bank, and the estimator, the selector configured toaccept one or more of the feedback signals or signals generatedtherefrom, to calculate one or more measured characteristics from thefeedback signals, to compare one or more model characteristics to themeasured characteristics to select a best fit model from the model bank,and to load, enable, and/or select an associated best fit model foroperation within the estimator.

In aspects, some non-limiting examples of characteristic and/or feedbacksignal include one or more forms of feedback (e.g., current, voltage,impedance characteristics, excursion levels, voice coil temperature,microphone feedback, histories thereof, etc.), device level feedback(e.g., acceleration, rotational movement, user settings, historiesthereof, etc.), ambient feedback (e.g., temperature, humidity, altitude,local pressure, histories thereof, etc.). In aspects, the characteristicmay be related to speaker impedance and the estimated state may berelated to speaker excursion.

In aspects, the system may include a feedback block in accordance withthe present disclosure coupled to an associated speaker and/or driver,configured to provide one or more feedback signals or signals generatedtherefrom to the system, a model bank in accordance with the presentdisclosure including a group of feedback estimating models eachassociated with a corresponding state estimating model, and configuredto calculate a value from one or more of the input signals, and aselector coupled to the feedback block, the model bank, and theestimator, the selector configured to compare one or more of the valuesto the feedback signals to select a best fit feedback estimating modelfrom the model bank, the selector configured to load, enable, and/orselect the corresponding best fit state estimating model for operationwithin the estimator.

In aspects, the feedback signals may be related to speaker currentand/or voltage, and the estimated state may be related to speakerexcursion.

In aspects, the protection block may include a compressor and/or limiterconfigured to accept the input signals, the compressor and/or limiterincluding one or more properties, one or more of which may be configuredby the estimated states and/or estimating signal.

In aspects, one or more components of the system may be configured toaccept a power constraint from an external power manager and/or togenerate a power prediction. In aspects, the power constraint and/orpower prediction may be used in the generation of the output signal.

In aspects, the power protection block may be configured to accept akinetic feedback signal representative of the movement of the speakerwithin an environment, and to use the kinetic feedback signal in thegeneration of the output signal.

In aspects, some non-limiting examples of kinetic feedback signalsinclude a linear acceleration, a rotational motion, a pressure change, afree-fall condition, an impact, or the like.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic of a nonlinear control system in accordancewith the present disclosure;

FIGS. 2a-e show aspects of a handset coupled with the head of a subjectand anatomical features of an ear in accordance with the presentdisclosure;

FIG. 3a-e show aspects of components of a nonlinear control system inaccordance with the present disclosure;

FIG. 4 shows a schematic of an adaptive control system in accordancewith the present disclosure;

FIGS. 5a-e show spectra related to acoustic coupling of a range ofhandsets to the ear of subjects in accordance with the presentdisclosure;

FIG. 6 shows a graphical description of a control algorithm for use in anonlinear control system in accordance with the present disclosure;

FIG. 7 shows the output of a method for fitting aspects of a nonlinearmodel in accordance with the present disclosure;

FIGS. 8a-b show aspects of nonlinear hysteresis models in accordancewith the present disclosure;

FIG. 9 shows a consumer electronics device for use with a control systemin accordance with the present disclosure;

FIGS. 10a-b show spectral representations of the power delivered to andimpedance of a speaker over a period of time in accordance with thepresent disclosure;

FIG. 11 shows aspects of a system for generating variables from signalsmeasured from a speaker in accordance with the present disclosure;

FIG. 12 shows aspects of an optionally multi-rate system for generatingvariables from signals measured from a speaker in accordance with thepresent disclosure;

FIG. 13 shows a semi-logarithmic graph outlining some non-limitingexamples of relationships between stress state and cycles to failure fora speaker in accordance with the present disclosure;

FIGS. 14a-c show aspects of systems for extracting parameters from oneor more signals measured in a system in accordance with the presentdisclosure;

FIGS. 15a-c show aspects of a system for controlling a speaker inaccordance with the present disclosure;

FIG. 16 shows aspects of a schematic of an active speaker control systemin accordance with the present disclosure;

FIGS. 17a-b shows aspects of methods for updating an adaptive model inaccordance with the present disclosure;

FIG. 18 shows aspects of a method for calculating one or more parametersfrom spectra in accordance with the present disclosure;

FIGS. 19a-g show aspects of techniques and relationships for derivingone or more speaker parameters and/or predicting the remaining lifetimeof a speaker in accordance with the present disclosure;

FIG. 20 shows a schematic of aspects of a speaker protection system inaccordance with the present disclosure.

FIGS. 21a-e show aspects of excursion estimators each in accordance withthe present disclosure.

FIGS. 22a-c show aspects of a speaker protection system in accordancewith the present disclosure.

FIGS. 23a-c show aspects of a speaker protection system in accordancewith the present disclosure.

FIGS. 24a-b show aspects a model selection process in accordance withthe present disclosure.

DETAILED DESCRIPTION

Particular embodiments of the present disclosure are describedhereinbelow with reference to the accompanying drawings; however, thedisclosed embodiments are merely examples of the disclosure and may beembodied in various forms. Well-known functions or constructions are notdescribed in detail to avoid obscuring the present disclosure inunnecessary detail. Therefore, specific structural and functionaldetails disclosed herein are not to be interpreted as limiting, butmerely as a basis for the claims and as a representative basis forteaching one skilled in the art to variously employ the presentdisclosure in virtually any appropriately detailed structure. Likereference numerals may refer to similar or identical elements throughoutthe description of the figures.

The term consumer electronic device is meant to include, withoutlimitation, a cellular phone (e.g., a smartphone), a tablet computer, alaptop computer, a portable media player, a television, a portablegaming device, a gaming console, a gaming controller, a remote control,an appliance (e.g., a toaster, a refrigerator, a bread maker, amicrowave, a vacuum cleaner, etc.) a power tool (a drill, a blender,etc.), a robot (e.g., an autonomous cleaning robot, a care giving robot,etc.), a toy (e.g., a doll, a figurine, a construction set, a tractor,etc.), a greeting card, a home entertainment system, an active speaker,a media accessory (e.g., a phone or tablet audio and/or videoaccessory), a sound bar, and so forth.

The term speaker is meant to include, without limitation, a componentfor rendering audio, a speaker, a receiver, or the like. In aspects, aspeaker may be configurable, such that it may be used as a microphonefor monitoring an adjacent acoustic field. In aspects, a speaker may beused to render an audio stream for a user, and/or produce an acousticwatermark, intended for calibration, feedback, determination of anacoustic impedance, an acoustic coupling, an acoustic volume between thespeaker and a surface, proximity of a surface to a speaker, etc. Inaspects, a receiver may be directed towards a speaker for placement nearan ear of a user, within an ear facing component of a consumer device,within an earpiece, etc.

The term input audio signal is meant to include, without limitation, oneor more signals (e.g., a digital signal, one or more analog signals, a5.1 surround sound signal, an audio playback stream, etc.) provided byan external audio source (e.g., a processor, an audio streaming device,an audio feedback device, a wireless transceiver, an ADC, an audiodecoder circuit, a DSP, etc.).

The term acoustic signature is meant to include, without limitation, theaudible or measurable sound characteristics of a consumer electronicdevice and/or a component thereof (e.g., a speaker assembly, withenclosure, waveguide, etc.) dictated by its design that influence thesound generated by the consumer electronic device and/or a componentthereof. The acoustic signature may be influenced by many factorsincluding the speaker design (speaker size, internal speaker elements,material selection, placement, mounting, covers, etc.), device formfactor, internal component placement, screen real-estate and materialmakeup, case material selection, hardware layout, and assemblyconsiderations amongst others. Cost reduction, form factor constraints,visual appeal and many other competing factors are favored during thedesign process at the expense of the audio quality of the consumerelectronic device. Thus, the acoustic signature of the device maydeviate significantly from an ideal response. In addition, manufacturingvariations in the above factors may significantly influence the acousticsignature of each device, causing further part to part variations thatdegrade the audio experience for a user. Some non-limiting examples offactors that may affect the acoustic signature of a consumer electronicdevice include: insufficient speaker size, which may limit movement ofair necessary to re-create low frequencies, insufficient space for theacoustic enclosure behind the membrane which may lead to a highernatural roll-off frequency in the low end of the audio spectrum,insufficient amplifier power available, an indirect audio path betweenmembrane and listener due to speaker placement often being on the backof a TV or under a laptop, relying on reflection to reach the listener,among others factors.

An acoustic signature may be influenced by one or more environmentalaspects, such as the proximity of a component to a surface (e.g. an ear,a face, a thumb pad, etc.), degree of coupling with the ear of a subject(i.e. the extent of the gasket formed when the component is coupled tothe ear), temperature, humidity, aging, dust accumulation, etc. Inaspects, the degree of coupling with an ear may be influenced by suchfactors as the positioning of the component near the ear, the anatomy ofthe ear, clothing and/or jewelry worn on or around the ear, the pressurewith which the component or device is pressed against the ear, and thelike.

An acoustic signature may include one or more nonlinear aspects relatingto material selection, design aspects, assembly aspects, etc. that mayinfluence the audio output from the associated device, causing sucheffects as intermodulation, harmonic generation, sub-harmonicgeneration, compression, signal distortion, bifurcation (e.g., unstablestates), chaotic behavior, air convective aspects, and the like. Somenon-limiting examples of nonlinear aspects include eddy currents, conepositional nonlinearities, coil/field nonlinearities, DC coildisplacement, electromechanical nonlinearities (e.g., magnetic and/orE-field hysteresis), viscoelastic and associated mechanical aspects(e.g., suspension nonlinearities, nonlinear damping, in the spider,mounting frame, cone, suspension geometry, etc.), assemblyeccentricities, driver characteristics, thermal characteristics,acoustic radiation properties (e.g., radiation, diffraction,propagation, room effects, convection aspects, etc.), audio perceptioncharacteristics (e.g., psychoacoustic aspects), and the like.

Such nonlinear aspects may be amplitude dependent (e.g., thermallydependent, cone excursion dependent, input power dependent, etc.), agedependent (e.g., changing over time based on storage and/or operatingconditions), operating environment dependent (e.g., based on slow onsetthermal influences), aging of mechanical and/or magnetic dependent(e.g., depolarization of associated magnetic materials, aging of rubberand/or polymeric mounts, changes associated with dust collection, etc.),dependent upon part-to-part variance (e.g., associated withmanufacturing in precision, positioning variance during assembly, variedmounting pressure, etc.), dependent on coupling with a nearby object(e.g. an ear, a head, a face, etc.), and the like.

A control system in accordance with the present disclosure may beconfigured to compensate for one or more of the above aspects, includingduring playback of a general audio stream. Such nonlinear controlsystems may be advantageous to effectively extend the audio qualityassociated with an audio stream to the limits of what the associatedhardware can handle.

In aspects, a control system in accordance with the present disclosuremay be configured to compensate for acoustic impedance variationassociated with coupling between one or more components (e.g. atransducer, a speaker, a receiver, a microphone, etc.) and one or morekey surfaces (e.g. an ear, a face, a head, a door lock, etc.), duringuse in an environment (e.g. during casual use in a noise environment,during a phone call, etc.). In such aspects, the control system maycompensate by estimating the acoustic impedance between component andthe surface, acoustic leakage between the component and the surface,acoustic load on the component caused by being in close proximity to thesurface, etc. In aspects, such compensation may be determined bymeasuring one or more current and/or voltage parameter on the componentduring use, and/or capturing one or more acoustic signal on one or moreassociated components, and estimating the acoustic impedance or avariation in the acoustic impedance therefrom using one or morealgorithms as described herein.

In aspects, the control system may be configured to accept an acousticfeedback signal from a microphone on the consumer electronic device(e.g. a microphone positioned near the mouthpiece, a microphonepositioned near the earpiece, etc.). The feedback signal may be directedinto an algorithm for assessing background noise, removing backgroundnoise, assessing acoustic impedance, a coupling between a speaker and amicrophone, etc. In aspects, such feedback may be incorporated into aparametric extraction algorithm, configured to accept the feedbacksignal and generate a control parameter related to the environment,acoustic coupling, surface proximity of a speaker, degree of gasket,speaker excursion, of the like. Relating to feedback to assessbackground noise, the control system may be configured to generate acontrol parameter related to the level or a characteristic of thebackground noise, to adjust an acoustic gain parameter based on thelevel of the background noise, etc.

In aspects, a handset in accordance with the present disclosure may beconfigured for placement up to the head of a subject. For the sake ofdiscussion, a handset may include a region labeled as an earpiece, theearpiece being that region of the handset that is meant to be coupled tothe ear of the subject during use. The handset may also include a regionlabeled as a mouthpiece, the mouthpiece being that region of the handsetthat is meant to interface with the mouth of the subject.

In some applications, operational stresses on one or more elements of aspeaker may be estimated by prediction of the temperature of the speakerin service. In many cases, to adequately protect the speaker, thespeaker temperature may be measured with an accuracy of approximately+/−5 degrees centigrade. Oftentimes, the maximum allowed speaker coiltemperature is typically 105 degrees centigrade while a typicaloperating temperature may be 80-90 degrees centigrade. Thus, areasonably small operating window may exist within which to manage heatdissipation of the speaker (roughly 10-20 degrees centigrade). As aresult, an accurate temperature measurement for the speaker coil may beadvantageous in a practical speaker protection system.

Often, the temperature changes in a speaker may be estimated bycalculating the DC resistance of the speaker. This resistance isdependent on the temperature as a result of the temperature coefficientof the wire used for the speaker coil. However, the impedance may varydramatically due to process variations during production. For a typicalmobile phone speaker, the nominal resistance may vary by approximately+/−10 percent (e.g., for typical temperature dependence values, willlead to a temperature offset of approximately +/−25 degrees Centigrade).

In aspects, a speaker protection system and a speaker control system aredisclosed including an excursion estimator (e.g., an estimate for thevoice coil excursion of an associated speaker). In aspects, theexcursion estimator may include or be coupled to a plurality of models,each model configured to estimate a speaker excursion parameter. Inaspects, the plurality of models may be derived for a class of speakers(e.g., units produced within a particular product family, selected frommanufacturing based testing of a product, or product family, etc.). Themodels may be configured to estimate speaker excursion from an inputsignal. In aspects, the excursion estimator may select a worst casemodel (or the worst case output from the plurality of models at anygiven time in order to make a worst case estimate). In aspects, afeedback signal (e.g., a voltage, and/or current feedback, a devicecharacteristic, etc.) may be extracted from or measured on the speakerduring operation and compared (e.g., within the estimator) with one ormore of the models, so as to select a best fit model from the pluralityof models to represent the device at any given time during operationthereof. In aspects, the excursion parameter may be combined with thefeedback to determine a proximity model, related to the distance,orientation, acoustic coupling, etc. between the speaker and a nearbysurface, such as an ear of a subject.

In aspects, a speaker protection system and/or control system inaccordance with the present disclosure may be configured in an entirelyfeed-forward or adaptive feed-forward fashion. In such a configuration,an excursion estimation of the speaker may be made from one or more ofthe estimators without explicit excursion feedback from the speaker oran associated driving circuit (e.g. such as via measurement of one ormore characteristic associated with the electrical impedance of thespeaker, via one or more audio feedback signals, etc.). The same controlsystem may be configured to generate an acoustic quality controlparameter from the same feedback signal, and adjust an audio streamrendered on the speaker so as to improve sound quality for an associateduser.

Relating to speaker protection, a plurality of models may be selected soas to ensure, for a given device or device family, that the estimatedexcursion (e.g., from one or more of the models) is always a worst casecondition. Such a configuration may be advantageous for providingspeaker protection without the need for additional feedback relatedhardware, and/or additional computational resources (e.g., additionalcomputational resources required for, real-time computation of models,spectral model calculation, testing procedures, etc.).

In aspects, the plurality of models may be generated during manufacture,updated post launch, etc. In aspects, a virtual model library may begenerated and updated throughout the lifetime of the product. In such aconfiguration, the virtual model library may be updated, sub-classes ofmodels from the library may be sent to devices in the field (e.g., aspart of an update procedure, etc.). In aspects, sub-classes of modelsmay be defined based upon manufacturing lots, aging related feedback(e.g., changes in impedance over time), user usage case classification(e.g., heavy user, mobile user, extreme user, light user, etc.). Such anupdate may be performed as part of a firmware update, as a way ofpreventing degradation of the speaker (e.g., to reduce the speakeroutput for a certain sub-class, or user class, etc., so as to extendworking life, or reduce in-field failures, etc.). In aspects, the modelsthat may be loaded onto a device could be derived from sub classesassociated with a product ID number (e.g., a known manufactured batch ofspeakers, etc.).

In aspects, the system may include one or more models representative ofa common failure mode (e.g., over-excursion related damage, heatingrelated property changes, fatigue related damage, impact related damage,leakage related failure, adhesive detachment, etc.). In aspects, thesystem may include a test process to determine if an associated speakerunit is operational, or if the speaker unit has failed, perhaps due toan event, wear-and-tear, etc.

In aspects, one or more of the models may include a failure mode modelfor a leaking case scenario. Such a configuration may be advantageous indebugging failures associated with other aspects of the device (e.g.,such as a leaky phone case, etc.) which may impact the performance ofthe speaker.

In aspects, one or more of the models may include a free air testcondition (e.g., performed over a range of temperatures), and/or ablocked vent condition such that a range of failures may be predictedwithout excessive computational effort or complex models.

In aspects, one or more models may be updated during use. In onenon-limiting example, a model for the acoustic impedance, acousticcoupling between an earpiece speaker and a mouthpiece microphone,acoustic coupling between an earpiece speaker and an earpiecemicrophone, an assessment of background noise, and/or a parameterrepresentative of the receiver-to-ear gasket may be generated inreal-time or pseudo real-time, and one or more control algorithmsupdated in the system so as to advantageously adjust an audio streamduring rendering to adjust for one or more receiver-to-ear positioningeffects, so as to maintain the audio quality of the audio streamdelivered to the ear of the subject, the removal of background noisefrom the audio stream, limiting of the associated speaker excursion soas to limit the possibility of damaging the associated speaker and/orthe eardrum of the ear to which the speaker has been placed.

In aspects, during periods of time, it may be the case that the controland/or protection system may not successfully identify the desiredsystem states, a best fit may not be determined, etc. Such a conditionmay occur, for example, if the speaker properties change dramaticallyduring use (e.g., if the speaker gets blocked, damage occurs due to animpact, an output vent becomes plugged, a finger covers an output vent,etc.). The system, selector, and/or protection block, may include a safeoperating condition into which it may operate during such periods. Inaspects, the safe operating mode may include over estimating the speakerstates from the estimates, summing the estimates to form a worst casestate estimate, assessing a group of damage models, diagnosing thecondition, running a test, uploading one or more state estimates to adata center, or the like. The system may be configured to continueassessing the states, and/or characteristics during such a period todetermine if the system has returned to a normal operating state.

In aspects, the feedback signal may be used within or in communicationwith the estimator to compare one or more speaker characteristics withthose predicted by and/or associated with one or more of the models todetermine the best fit to the actual device at any given period in time.In aspects, the estimator may include means for loading the best fitmodel into a real-time estimator block, for selecting between two ormore “nearest” fit models, etc. Such a configuration may be advantageousfor effectively forming a worst case excursion estimate while operatingwith very little computational overhead. In aspects, the selectionprocess may be adaptive, may be performed within a cloud service (e.g.,offloaded from a user device), etc.

In aspects, there is provided a method for tracking field operation ofaudio devices and/or maintaining suitable operation thereof throughouttheir intended lifetime, including periodically collecting feedbacksignals from a plurality of devices in the field, analyzing the feedbackto compare each individual device against a master model set, andupdating a device in the field based upon the feedback signal and/or thecomparison. In aspects, such feedback signal collection may includecollecting speaker feedback (e.g., current, voltage, impedancecharacteristics, excursion levels, voice coil temperature, microphonefeedback, histories thereof, etc.), device level feedback (e.g.,acceleration, rotational movement, user settings, histories thereof,etc.), ambient feedback (e.g., temperature, humidity, altitude, localpressure, histories thereof, etc.). One or more of the collected signalsmay be used in the analysis or in comparison with the master model set,etc.

In aspects, a system in accordance with the present disclosure mayinclude calculating a device and/or device-environment coupledcharacteristic such as impedance, resonant frequency, quality factor,resistance, etc. and monitor how that characteristic changes over time(e.g., as implemented as part of a specific test protocol, as part of aslow extraction algorithm, peak finding algorithm, or the like). Inaspects, the system may be configured to periodically compare themeasured characteristic with the characteristics of the model class(e.g., the plurality of representative models) to better pick a nearestestimator, which may then be used to (potentially gradually) update anestimator, which may be running all the time in parallel. In aspects,changes in the characteristic, changes in the selected model, etc. maybe relayed to a data center (e.g., a cloud based data center, etc.) forfeedback, product decision making, consideration of updates, etc.

In aspects, the system may be configured to predict a parametricrelationship, such as an ear-gasket parameter from the calculation. Sucha parametric relationship may be used to update a control algorithm, atransfer function, or the like, so as to adjust the acoustic output ofthe device accordingly during use. In aspects, the system may beconfigured to monitor impedance on the receiver coupled with the ear. Amap, look-up table, or the like may be provided to correlate one or moreaspects of the measured impedance to an ear-gasket parameter. Such a mapmay be generated during development, during testing on a head and torsosimulator (HATS), from real-time feedback on consumer use and fieldtesting, etc. In aspects, the system may include a proximity sensor(e.g. an infrared sensor positioned near the receiver, etc.), configuredso as to measure an ear-receiver feedback parameter (e.g. distance tothe ear, proximity to the ear, local signal associated with positioningof the proximity sensor next to associated ear tissues, etc.). Theresulting proximity feedback parameter may be used to index into theimpedance look-up table, so as to update one or more control parameters,to adjust an output parameter from the associated receiver, transducer,speaker, or the like.

In aspects, the system, handset, etc. may include one or more additionalreceivers (e.g. microphones, speakers, etc.), such as are becoming morecommon on handsets and tablets. One non-limiting example is a handsetwith stereo configurable transceivers (i.e. a handset with configurablemicrophones/speakers positioned at each end thereof). In such aconfiguration, the transceivers may be configured in speaker and/ormicrophone mode so as to establish the degree of ear-gasket formedduring use. In one non-limiting example, a handset with a speakerpositioned to interface with the ear of a subject, and a microphonepositioned so as to interface with the mouth of a subject, may beconfigured such that the microphone records the audio stream generatedby the speaker during use. The recorded audio stream may be coupled toan adaptive algorithm to predict the acoustic transfer function betweenthe speaker and the microphone. The resulting acoustic transfer functionmay be related to the ear-gasket formed between the speaker, thehandset, and the ear of the subject at any particular time during use.One or more aspects of the predicted acoustic transfer function may beused to index a look-up table of control parameters, parametricallyalter a control parameter, used to select a transfer function, or thelike during use. Thus an adaptive means for adjusting the audio outputof the speaker without the need for additional hardware may be achieved.

In aspects, on a handset with configurable receivers, the ear-sidereceiver may be configured as a microphone, and the mouth side receivermay be configured as a speaker, or vice versa. The generated audiostream recorded with the corresponding microphone and used to generatethe corresponding acoustic impedance, and/or parameter there between.Such a configuration may be advantageous to estimate the extent of thegasket between the ear and the device at any given time during use. Oncethe extent of the gasket is estimated, it may be used as part of anadaptive control parameter, as an index to a look-up table, etc. so asto adjust the audio stream, so as to maintain sound quality perceived bythe subject during use.

In aspects, a system in accordance with the present disclosure mayinclude an adjustable compressor configured to clamp the input signal ora signal generated there from, the compressor configured to adjust adegree of clamping based upon the estimated excursion, a system event(e.g., a jolt, a free-fall condition, an impact condition, change in anambient parameter, etc.), a device input (e.g., acceleration, microphonemeasured audio output, etc.), an environmental input (e.g., a change inlocal pressure, etc.).

In aspects, the degree of signal compression may be influenced by anevent, such as an impact, a free fall condition (e.g., in anticipationof an impact), establishment and/or loss of an ear-device gasket, etc.Upon detection of such a condition, the compressor may be configured toclamp the input signal or a signal generated therefrom before sendingthe clamped signal onwards toward the associated speaker. In aspects,the clamping may be gradually released after to the event (baring anadditional related event), so as to slowly bring the speaker back to anoptimal state of operation. In aspects, a related system may includefunctionality for testing the device post event, etc. in order todetermine if any properties thereof have changed due to the eventitself.

In aspects, an event may include receiving a free-fall condition from anassociated accelerometer, receiving an impact condition (e.g., an impactof greater than 5 G, greater than 10 G, etc.), identifying placement ofthe speaker against a surface, establishment of a substantial ear-devicegasket (e.g., establishment of a partial gasket to an ear, establishmentof a substantially complete gasket to an ear, etc.). During as well asafter such events, the system may be configured to clamp the speakeroutput and gradually relax that compression, so as to suppress anunstable operating mode (e.g., such as a rocking mode, which may beexcited during the event). In aspects, such events (e.g., free-fall,impact, etc.) may be relayed via the associated sensor itself, as aninterrupt flag, etc. (e.g., as a “free-fall” related system interrupt,etc.).

In aspects, there is provided a method for testing a device to determinethe appropriate excursion estimating models for implementationthereupon. The method may include capturing an input/output historyduring a period of operation (e.g., during a period of heavy usage,during a period of normal usage, during an ear positioning testprocedure, during a self-diagnostic test, during music playback, etc.).The captured histories may be compared against master models for thedevice family to determine the most appropriate model sub-class for thedevice. In aspects, the test procedure may be used to select and/orenable one or more appropriate excursion models for predicting theexcursion of a particular speaker. In aspects, the test procedure may beperformed remotely from the device (e.g., offloaded histories may beanalyzed in a data center, a cloud service, etc.). In aspects, theprocedure may include updating the master models, performing a deviceupgrade, etc.

In aspects, there is provided an ear positioning test procedure, theprocedure including prompting a user to orient a handset to the earthereof, and/or to adjust the positioning of the handset to the ear, toplay and/or record an audio stream during the test, and to establish a“preferred positioning” between the handset and the ear, to establish aset of acoustic impedance models/parameters associated with multiple“preferred positions”, etc. One or more models established during suchtesting may be used to generate an initial model bank, one or moreparameters in a look-up table, one or more datasets reported to acloud-based data center, etc. One or more of the models/parameters maybe used as the initial basis for adjusting a future audio stream playedto the subject. In aspects, one or more acoustic impedance models may begenerated and/or updated in real-time or pseudo real-time during use,updated over time to reflect changing user habits, etc.

In aspects, during an ear positioning test procedure, proximityinformation may be obtained from an associated proximity sensor or thelike. Along with other measured information (such as current, voltage,calculated impedance, etc.). The proximity sensor may be configured tomonitor a signal related to the positioning of the headset and/ortransceiver to the ear and/or face of the subject, a near-fieldpositioning aspect of the handset against the ear, relate a contactpressure (e.g. such as is determined by a reflected light measured bythe proximity sensor when the device is pressured against a tissuesurface, etc.). In aspects, the proximity sensor signal may be providedas an input to an associated look-up table, as model parameter, etc. inconjunction with transducer impedance estimates, etc.

In aspects, the handset may include an image sensor (e.g. a front facingcamera, etc.), for obtaining information about a subject during use. Inaspects, the ear positioning test may include capturing one or moreimages of the ear of the subject. The image(s) may be correlated againstan anatomical reference database, or the like. The resulting gasketmodels, and/or acoustic impedance profiles may be compared againstpopulation norms from the anatomical reference database. In this aspect,a big data relationship between acoustic impedance measurements, eargasket measurements, etc. may be correlated with anatomical statisticssuch as ear shape, relative handset to ear positioning, etc. Suchinformation may be useful for generating deep learning algorithms,generating more accurate model parameters, establishing user specificmaster models, from which to orient an adaptive model (i.e. so as tolimit the amount of adaptation necessary in order to optimize the soundquality for a subject during use, etc.).

In aspects, one or more master models may be constructed frommanufacturing based sample testing, from an ear positioning testprocedure, from a HATS test protocol, from virtualized testing whereinthe tolerances (e.g., from the speaker manufacturer's test data,characterization data, etc.), from a population database, wherein one ormore speaker parameters (e.g., force factor, compliance, and otherThiele-Small parameters, etc.) may be entered into an associatedsimulator (e.g., within a system characterization toolkit, etc.) togenerate the corresponding master model set. Thus, a master model setmay be constructed from a combination of limited real-world tests (e.g.,from 10-100 production units, etc.), and a combination of statistical ormeasured tolerance ratings (e.g., from a speaker manufacturer, fromexcursion and impedance curves), from realistic user generated data,with the respective T.S. parameters for associated models. Thus, thesimulator may be configured to vary one or more of the basic parameterswithin the tolerance limits and perform one or more (e.g., tens,thousands, etc.) of virtual measurements following the behavior of thereal measured production units.

In aspects, the test procedure may include one or more system and/orspeaker nonlinearities. For example, and without limitation, in the testprocedure, the compressor nonlinearities could be considered (e.g.,estimator outputs could be run through the compressor to get moreaccurate values). So as to provide more accurate sub-class estimates fora particular device in the field, etc.

In aspects, there is provided a cloud service configured to collectinput/output histories, and/or configuration data from one or moredevices in the field (e.g., post purchase), during a routine updatecheck, etc. In aspects, the cloud service may be configured to generateone or more device characteristics (e.g., impedance curves, speakerparameters, ear-device acoustic impedance estimates, etc.), and comparethe obtained information with one or more metrics (e.g., characteristicsrelated to device failures, lifetimes, aging criteria, groups of failureprone devices, etc.) so as to improve estimation models (e.g., sent tothe devices as updates, etc.), to improve acoustic quality among apopulation base, to categorize a particular device in terms of aging,predicting lifetime, classifying failure types, predicting failuretypes, classifying user types (e.g., heavy, light users, etc.),combinations thereof, or the like.

In aspects, such information may be used to determine how devicecharacteristics change over time (e.g., how speaker compliance, resonantmodes, etc. age with use), and may be used as part of a field updateprocess in order to counteract impending failures (e.g., predict basedon the collected data, which devices are likely to fail in the field andalter the estimators or clamping parameters associated therewith inorder to circumvent failure, extend device lifetimes, etc.).

FIG. 1 shows a schematic of a control system (e.g. an acoustic controlsystem, a nonlinear control system, etc.) in accordance with the presentdisclosure. The nonlinear control system includes a controller 10configured to accept an input signal 1 from an audio source (notexplicitly shown) and one or more states 35. The system may include amodel and/or observer 30 (referred to herein as model 30 for the sake ofdiscussion), configured to generate the states 35. The controller 10 maygenerate one or more control signals 15 to drive an associated audiosystem 20. The control signals 15 may be fed to the model 30 forinclusion into the estimation of the states 35. The audio system 20 mayproduce one or more feedback signals 25, which may be directed to themodel 30 for use in generating the states 35.

In aspects, the controller 10 may be configured to produce a systemfeedback signal 12 for delivery to one or more related systems such as apower management system (not explicitly shown). In aspects, the systemfeedback signal 12 may be a prediction of future power usage by theaudio system 20. Such a system feedback signal 12 may be used by one ormore related systems (e.g., a power management system) to control powerdistribution, to balance power among other system components, etc.

The controller 10 may include a control strategy based upon one or moreof adaptive control, hierarchical control, neural networks, Bayesianprobability, backstepping, Lyapunov redesign, H-infinity, deadbeatcontrol, fractional-order control, model predictive control, nonlineardamping, state space control, fuzzy logic, machine learning,evolutionary computation, genetic algorithms, optimal control, modelpredictive control, linear quadratic control, robust control processes,stochastic control, combinations thereof, and the like. The controller10 may include a full non-linear control strategy (e.g., a sliding mode,bang-bang, BIBO strategy, etc.), as a linear control strategy, or acombination thereof. In one non-limiting example, the controller 10 maybe configured in a fully feed-forward approach (e.g., as an exactinput-output linearization controller). Alternatively, additionally orin combination, one or more aspects of the controller 10 may include afeed-back controller (e.g., a nonlinear feedback controller, a linearfeedback controller, a PID controller, etc.), a feed-forward controller,combinations thereof, or the like.

A controller 10 in accordance with the present disclosure may include aband selection filter (e.g., a bandpass, low pass filter, one or moredigital biquad filters, etc.) configured so as to modify the inputsignal 1 to produce a modified input signal (e.g., an input signal withlimited spectral content, spectral content relevant to the nonlinearcontrol system only, etc.). In one non-limiting example, the controller10 may include a filter with a crossover positioned at approximately 60Hz. The nonlinear control may be applied to the spectral content belowthe cross over while the rest of the signal may be sent elsewhere in thesystem, enter an equalizer, etc. The signals may be recombined beforebeing directed towards the audio system 20. In a multi-rate example, thesignals may be downsampled and upsampled accordingly, based on theirspectral content and the harmonic content added by the nonlinearcontroller 10 during operation. Such a configuration may be advantageousfor reducing the computational load on the control system duringreal-time operation.

The model 30 may include an observer and/or a state estimator. A stateestimator (e.g., an exact linearization model, a feed forward model, oneor more biquad filters, etc.) may be configured to estimate the states35 for input to the controller 10. The state estimator may include astate space model in combination with an exact input-outputlinearization algorithm in order to achieve this function, among otherapproaches. One or more aspects of the model 30 may be based upon aphysical model (e.g., a lumped parameter model, etc.). Alternatively,additionally, or in combination, one or more aspects of the model 30 maybe based upon a general architecture (e.g., a black box model, a neuralnetwork, a fuzzy model, a Bayesian network, etc.). The model 30 mayinclude one or more parametrically defined aspects that may beconfigured, calibrated, and/or adapted to better accommodate thespecific requirements of the given application.

One or more model selection processes in accordance with the presentdisclosure may be used to configure, enable, and/or select one or morestate estimator models and/or control system models for estimating thestates 35, the system feedback signal 12, and/or the control signal 15.In aspects, the observer 30 may be configured to generate a state 35 ormetric against which to compare a predicted value (e.g., an excursionprediction, an impedance prediction, a speaker characteristic, etc.) soas to select a model, adapt a model, etc. for purposes of control and/orspeaker protection.

The feedback signals 25 may be obtained from one or more aspects of theaudio system 20. Some non-limiting examples of feedback signals 25include one or more temperature measurements, impedance, drive current,drive voltage, drive power, one or more kinematic measurements (e.g.,membrane or coil displacement, velocity, acceleration, air flow, etc.),sound pressure level measurement, local microphone feedback, ambientcondition feedback (e.g., temperature, pressure, humidity, etc.),kinetic measurements (e.g., force at a mount, impact measurement, etc.),B-field measurement, combinations thereof, and the like.

The states 35 may be generally determined as input to the controller 10.In one non-limiting example, the states 35 may be transformed so as toreduce computational requirements and/or simplify calculation of one ormore aspects of the system. In aspects, the states 35 may be used toconfigure, enable, and/or select one or more estimators within thecontroller 10.

The control signals 15 may be delivered to one or more aspects of theaudio system 20 (e.g., to a driver included therein, to a speakerincluded therein, etc.).

The model 30 may include an observer (e.g., a nonlinear observer, asliding mode observer, a Kalman filter, an adaptive filter, a leastmeans square adaptive filter, an augmented recursive least squarefilter, an extended Kalman filter, ensemble Kalman filter, high orderextended Kalman filters, a dynamic Bayesian network, etc.). In onenon-limiting example, the model 30 may be an unscented Kalman filter(UKF). The unscented Kalman filter may be configured to accept thefeedback signal 25, the input signal 1, and/or the control signal 15.The unscented Kalman filter (UKF) 30 includes a deterministic samplingtechnique known as the unscented transform to pick a minimal set ofsample points (e.g., sigma points) around the mean nonlinear function.The sigma points may be propagated through the non-linear functions,from which the mean and covariance of the estimates are recovered. Theresulting filter may more accurately capture the true mean andcovariance of the overall system being modeled. In addition, UKF do notrequire explicit calculation of Jacobians, which for complex functionsmay be challenging, including on a resource limited device.

The UKF algorithm includes weight matrices that depend on the designvariables α, β and κ. The variable α may be configured between between 0and 1, β may be set equal to 2 (e.g., if the noise profile is roughlyGaussian), and κ is a scaling factor that may generally be set equal tozero or generally 3−n, where n is the number of states. Generallyspeaking, κ should be nonnegative to ensure the covariance matrix to bepositive semi-definite. For purposes of discussion, λ is introduced anddefined as:

λ=α²(n+κ)−n  Equation 1

and the calculations of the weights are:

W _(m) ⁰=λ/(n+λ)  Equation 2

W _(c) ⁰=λ/(n+λ)+1−α²+β

W _(m) ^(i)=1/(2(n+λ)), i=1, 2, . . . , 2n

W _(c) ^(i)=1/(2(n+λ)), i=1, 2, . . . , 2n

which are assembled into:

W _(m=)[W _(m) ⁰ W _(m) ¹ . . . W _(m) ^(2n)]^(T)

W _(c=)[W _(c) ⁰ W _(c) ¹ . . . W ^(2n) _(c)]^(T)  Equation 3

The prediction step may be defined by a sigma-point vector:

X _(k-1)=[m _(k-1) . . . m _(k-1)]+√{square root over (n+λ)}[0 √{squareroot over (P _(k-1))}−√{square root over (P _(k-1))}]  Equation 4

based on the prior mean, m_(k-1), and covariance, P_(k-1). The vectorcan be divided into single sigma points W_(k-1) ^(j) for j=1, 2, . . . ,2n+1. The points are then propagated through the non-linear function:

{circumflex over (X)} _(k) ^(j) =f({circumflex over (X)} _(k-1) ^(j) , u_(k-1))  Equation 5

By assembling all {circumflex over (X)}_(k) ^(j) as

{circumflex over (X)}_(k)=[{circumflex over (X)} _(k) ¹ . . .{circumflex over (X)} ^(2n+1) _(k)]  Equation 6

with the resulting mean and covariance predicted by:

m _(k) ={circumflex over (X)} _(k) W _(m)

P _(k) ={circumflex over (X)} _(k) W _(c) {circumflex over (X)} _(k)^(T) +Q  Equation 7

where the covariance of the process noise is denoted Q.

The updated sigma points are given by:

X _(k)=[ m _(k) . . . m _(k)]+√{square root over (n+λ)}[0 √{square rootover (P _(k))}−√{square root over (P _(k))}]  Equation 8

The resulting sigma points are then propagated through the measurementfunction:

Z _(k) ^(j) =h(X _(k) ^(j))  Equation 9

and a corresponding Kalman filter gain is calculated:

S _(k) =Z _(k) W _(c) Z _(k) ^(T) +R

C _(k) =X _(k) W _(c) Z _(k) ^(T)

K _(k) =C _(k) S ⁻¹ _(k)  Equation 10

The matrix R is the covariance matrix for the measurement noise.Finally, the estimated mean and covariance are updated according to:

P _(k) =P _(k) −K _(k) S _(k) K _(k) ^(T)

m _(k) =m _(k) +K _(k)(z _(k) −ū _(k))

ū _(k) =Z _(k) W _(m)  Equation 11

In one non-limiting example, the unscented Kalman filter may beaugmented (e.g., to form an augmented unscented Kalman filter [AUKF]).The AUKF includes an augmented state vector for the process andmeasurement noise calculation thus including non-symmetric sigma points.The AUKF may be advantageous for capturing odd-moment information duringeach filtering recursion.

FIGS. 2a-e show aspects of handsets in accordance with the presentdisclosure coupled to the head of a subject. FIG. 2a shows a handset 210in accordance with the present disclosure. The handset 210 includes acontrol system 10 in accordance with the present disclosure, and one ormore transducers (e.g. microphones, speakers, configurable receivers,etc.), configured so as to couple in audio communication with thesubject 201. The handset 210 is held such that one or more of thetransducers (not explicitly shown), is positioned in the vicinity of theear 202 of the subject during use. The positioning of the handset 210 tothe ear 202 is considered variable, as the subject 201 may orient thehandset 210 to the ear 202 in a variety of ways, with differentpreferences, different environments (e.g. quiet, windy, noisy, etc.),during different lengths of phone call, different types of phone call(e.g. feelings toward the phone call participant, the subject nature ofthe call, etc.), jewelry and/or clothing on the subject 201 in thevicinity of the ear 202, etc.

FIG. 2b illustrates a schematic of a handset 215 in accordance with thepresent disclosure, the handset 215 including a microphone 217, and areceiver 219 (e.g. a speaker, a transducer, a configurable transducer,etc.). The receiver 219 is oriented so as to interface with the ear 202of a subject 201. The schematic shows an audio stream 218 beinggenerated by the receiver 219 interacting with the ear 202 of thesubject 201, but also, in aspects, with the microphone 217. Adjustmentof the position, mounting pressure to the ear 202, and the like may beperformed by the subject 201, so as to try and better couple thereceiver 219 against the ear 202. The control system 10 is configured soas to measure one or more aspects of the coupling between the receiver219 and the ear 202, and to adjust the audio stream 218 so as to improvethe listening experience for the subject 201. The handset 215 is shownwith a region designating an earpiece 222, and a mouthpiece 223, theregions 222, 223 essentially designating regions of the handset 215configured so as to interface with the mouth 203 of the subject 201(i.e. the mouthpiece 223), and the ear 202 of the subject 201 (i.e. theearpiece 222) in accordance with the present disclosure.

FIG. 2c illustrates a schematic of a handset 225 in accordance with thepresent disclosure, the handset 225 including a first configurablereceiver 226 (e.g. a transducer that may be configured as a microphone,a speaker, switch function between a microphone or speaker during use, aspeaker with self-sensing acoustic function, etc.) and a secondconfigurable receiver 227 (e.g. a transducer that may be configured as amicrophone, a speaker, switch function between a microphone or speakerduring use, a speaker with self-sensing acoustic function, etc.). Thefirst configurable receiver 227 is shown oriented so as to interfacewith the ear 202 of a subject 201. The schematic shows a first audiostream 228 being generated by the second configurable receiver 227interacting with the mouth and ear 202 of the subject 201, but also, inaspects, with the first configurable receiver 226. The schematic alsoshows a second audio stream 229 being generated by the firstconfigurable receiver 226 interacting with the ear 202 of the subject201 and also with the second configurable receiver 227. The relationshipbetween the rendered audio streams 228, 229 and the recorded streams(i.e. as recorded by one or more of the configurable receivers 226,227), may be analyzed by the control system 10 in accordance with thepresent disclosure. Adjustment of the position, mounting pressure of thehandset 225 against the ear 202, and the like may be performed by thesubject 201, so as to try and better couple the second configurablereceiver 227 against the ear 202. The control system 10 may beconfigured so as to measure one or more aspects of the coupling betweenthe second configurable receiver 227, the first configurable receiver226, and/or the ear 202, and to adjust the second audio stream 229 so asto improve the listening experience for the subject 201. In aspects, thefirst audio stream 228 and/or the second audio stream 229 may includeone or more diagnostic signals in accordance with the presentdisclosure. The diagnostic signals meant to query the speakers 226, 227and, in combination with the control system 10, adjust the audio stream229, 228 in order to improve the listening experience for the subject201.

FIG. 2d illustrates a typical ear 235 of a subject (not explicitlyshown). Parameters associated with the positioning of an associatedhandset against the ear 235 are shown. Such parameters include thetranslations 237, 239 in the vicinity of the ear, angle of orientation241 with respect to the ear axis 242, pressure application over the ear235, and out of plane orientation (not explicitly shown), all of whichmay contribute to the quality of the acoustic coupling between the ear235 of the subject and the associated handset (i.e. the speaker withinthe handset). Such acoustic coupling may include changes in the acousticleakage around the ear, changes in the acoustic impedance seen by thespeaker, changes in the air volume between the ear canal of the subjectand the speaker, etc. Such changes may be monitored by a system inaccordance with the present disclosure and adapted for during use, so asto improve the quality of the acoustic signal presented to the subject.In aspects, a proximity sensor, a front facing camera, or the like, maybe used to generate a position based reference for inclusion into thecontrol system adaption algorithm. In aspects, one or more of thetranslation parameter above, may be mapped to one or more imagesacquired by an associated imaging sensor. In aspects, one or more of thepositioning parameters may be extracted and/or estimated from a changein and associated current, voltage, or impedance measurement/predictionobtained during use. Such information may be used as a look-up parameterfor a table of control parameters, a model bank, etc. each in accordancewith the present disclosure.

Some anatomical markers of the ear 235 are shown and may be relevant tothe acoustic coupling between a speaker and the subject during use. FIG.2d includes some anatomical features of the external ear 235 of asubject including the helix 245, the scaphoid fossa 247, the antihelix(near 237), the cymba concha, the helix 251, the antitragus (near 242),the lobe 261, the intertragus notch 259, the cavum concha (near 249),the tragus 257, the crus of helix 255, and the triangular fossa 253.

During typical usage, a user will often try to establish a seal betweenthe handset and the ear 235 by sealing against the crus of helix 255 andthe antitragus and the lobe 261, by holding the handset cheek down southand against the helix 245 around the ear. With modern mobile phones itis generally challenging to use the helix 245 for sealing as the handsetis too small. So many users try to push the phone against the anti-helix(near 237) and the fossa-triangularis 253. As the helix 245 is often notavailable for sealing, there is a strong need to improve the acousticquality under such compromised conditions. Thus a control system inaccordance with the present disclosure may be advantageous for improvingthe acoustic quality of an audio stream for a subject when suchsub-optimal positioning is used during operation.

FIG. 2e shows an image series of anatomical variation of ears among apopulation of subjects. The image series demonstrates how significantlydifferent the anatomical variation of external ear features is amongsubjects. The anatomical variation of the ear may impact the ability togenerate a gasket effect, the acoustic coupling, and/or the acousticleakage between the ear and the handset. Thus the acoustic impedancebetween the ear and the handset transducer may vary dramatically amongusers, even for similar orientation and positioning of the handsetagainst the ear. The quality of the acoustic coupling of an associatedhandset to the ear of a particular subject may be highly variable, andnot something that can be predicted by simulation during development ofthe handset.

As can be seen, jewelry, body modification, a disease state (e.g.cauliflower ear, etc.), and the like may all impact the contact surfacesavailable against which a subject may press the handset during use. Inaspects, a method and/or system in accordance with the presentdisclosure may measuring the resulting acoustic impedance or arepresentation thereof, and generate a near optimal acoustic filterpersonalized not only for a particular user, but also for the presentorientation of the handset against the ear of the user. In aspects,variation in ear jewelry and/or clothing (such as hats, scarves, etc.),may influence the acoustic impedance approximation, thus a real-timeassessment of the acoustic impedance may be desirable to improve theaudio quality of the system for each user.

Some non-limiting examples of ear jewelry are also shown which mayimpact acoustic coupling between a speaker and the ear canal of thesubject. Such jewelry may further interfere with, and or alter theacoustic coupling between a handset speaker and the ear 235. FIG. 2eillustrates a range of common piercings, including a forward helix ring265, an industrial 267, a rook 283, a helix ring 269, a snug 269, aninner conch 281, an anti-tragus bar 273, a tragus ring 279, an upperlobe ring 275, and a standard lobe plug or tunnel 277. Such jewelry,particularly that near the concha (near 249) and the anti-helix (near237) can significantly affect the acoustic coupling between a nearbyheadset and the ear 235.

FIG. 3a-e show aspects of components of a nonlinear control system inaccordance with the present disclosure.

FIG. 3a shows aspects of a feed-forward controller 302 in accordancewith the present disclosure. The feed-forward controller 302 may beconfigured to accept an input signal 1 and a state vector 301 andgenerate one or more control signals 311. In a basic configuration, thefeed-forward controller 302 may include a target dynamics block 306configured to accept the input signal 1 or a signal derived therefrom(e.g., a modified input signal 303 a), and a state vector 301 or signalderived therefrom (e.g., a modified state vector 305), and optionally aflag 303 b (e.g., a signal generated by one or more components of thecontrol system), and generate a targeted output signal 307. The targetdynamics block 306 may be configured so as to provide a desiredtransformation for the input signal 1 (e.g., an equalizer function, acompressor function, a linear inverse dynamic function, additional addedharmonics, etc.).

The controller 302 may include an inverse dynamics block 308 configuredto compensate for one or more non-linear aspects of the audio system(e.g., one or more nonlinearities associated with the speaker, thedriver, the enclosure, etc.). The inverse dynamics block 308 may beconfigured to accept the targeted output signal 307, a state vector 301or signal derived therefrom (e.g., a modified state vector 305), andoptionally a flag 303 b (e.g., a signal generated by one or morecomponents of the control system), and generate one or more initialcontrol signals 309. The inverse dynamics block 308 may be configuredbased on a black or grey box model, or equivalently from a parametricmodel (such as the lumped parameter model outlined herein). Thus, thesystem may include a pure “black-box” modeling approach (e.g., a modelwith no physical basis, but rather a pure input-to-output behaviormapping that can then be compensated for). In some instances, aphysically targeted model may reduce the computational load on thenonlinear control system.

The controller 302 (e.g., a non-limiting implementation of a controller10, a feed-forward controller 210, etc.) may include a protection block304, configured to accept one or more input signals 1 and one or morestates 301 and optionally produce one or more modified input signals 303a, modified states 305, and/or a flag 303 b. The protection block 304may be configured to compare one or more aspects of the input signal 1,the state vector 301 or one or more signals generated therefrom (e.g.,an input power signal, a state power signal, a thermal state, coneexcursion, a thermal dynamic, a thermal approach vector, etc.). Theprotection block 304 may compare such information against a performancelimitation criteria (e.g., a thermal model, an excursion limitation, apower consumption limitation of the associated device [e.g., aconfigurable criteria], etc.) to determine how close the operatingcondition of the audio system is to a limit, the rate at which theoperating state is approaching a limit (e.g., a thermal limit), etc.

Such functionality may be advantageous for generating a look-aheadtrajectory for smoothly transitioning system gain, performance aspects,etc. so as to remain within the limitation criteria as well as reducethe probability of introducing audio artifacts based when applyinglimits to the system.

The protection block 304 may generate such information in terms of aflag 303 b (e.g., a warning flag, a problem flag, etc.), the flag 303 bconfigured so as to indicate a level of severity to one or more aspectsof the control system, to assist with parametrically limiting the outputof one or more aspect of the control system, etc. Alternatively,additionally, or in combination, the protection block 304 may directlyaugment the input signal 1, the states 301, so as to generate a modifiedinput signal 303 a or a modified state vector 305, so as to provide theprotection aspect without addition computational complexity to otheraspects of the control system.

The controller 302 may include a compressor and/or a limiter 310configured to accept the initial control signal 309, one or more states301 or signals generated therefrom (e.g., a modified state vector 305),or the flag 303 b. The limiter 310 may be configured to limit theinitial control signal 309 based on one or more aspects of the states305, the initial control signal 309, the flag 303 b, combinationsthereof, and the like. The limiter 310 may be configured to generate alimited control signal 311 for use by one or more components in thecontrol system. In aspects, the limiter 310 may be a compressor, with alimit configured based upon a predetermined criteria and/or the flag 303b. In aspects, the flag 303 b may be provided by or derived from anexternal processor (e.g., a system power manager, etc.), so as toprovide a constraint upon which the limiter 310 may function.

FIG. 3b shows a non-limiting example of an audio system 20 (e.g., 220,etc.) in accordance with the present disclosure. The audio system 20 mayinclude one or more transducers 318 (e.g., speakers, actuator, etc.).The term transducer 318 is meant to include, without limitation, acomponent or device such as a speaker suitable for producing sound(e.g., an audio signal 321). A transducer 318 may be based on one ofmany different technologies such as electromagnetic, thermoacoustic,electrostatic, magnetostrictive, ribbon, audio arrays, electroactivematerials, and the like. Transducers 318 based on different technologiesmay require alternative driver characteristics, matching or filteringcircuits but such aspects are not meant to alter the scope of thisdisclosure.

The audio system 20 may include a transducer module 332, which mayfurther include a transducer 318 and a circuit 316. The circuit 316 mayprovide additional functionality (e.g., power amplification, energyconversion, filtering, energy storage, etc.) to enable a driver 314external to the transducer module 332 to drive the transducer 318. Somenon-limiting examples of the circuit 316 (e.g., a passive filtercircuit, an amplifier, a de-multiplexer, a switch array, a serialcommunication circuit, a parallel communication circuit, a FIFOcommunication circuit, a charge accumulator circuit, etc.) are describedthroughout the present disclosure.

The circuit 316 may be configured with one or more sensory functions,configured so as to produce a speaker feedback 319. The speaker feedback319 may include a current signal, a voltage signal, an excursion signal,a kinetic signal, a cone reflection signal (e.g., an optical signaldirected at the cone of the speaker), a pressure sensor, a magneticsignal sensor (e.g., a field strength measurement, a field vector,etc.), combinations thereof, and the like. The speaker feedback signal319 may be configured for use by one or more components in the controlsystem.

The driver(s) 314 may be half bridge, full bridge configurations, andmay accept one or more PWM signals to drive either the correspondinghigh and low side drivers. The driver(s) 314 may include a class Damplifier, a balanced class D amplifier, a class K amplifier, or thelike. The driver(s) 314 may include a feedback circuit for determining acurrent flow, voltage, etc. delivered to the transducer(s) during use.The amplifier may include a feedback loop, optionally configured toreduce one or more nonlinearities in one or more transducers 318 and/orthe electrical components in the system.

The driver 314 may include one or more sensory circuits to generate adriver feedback signal 317. The driver feedback signal 317 may include apower signal, a current signal, an impedance measurement (e.g., aspectral measurement, a low frequency measurement, etc.), a voltagesignal, a charge, a field strength measurement, an aspect of a drivesignal 315, or the like.

In aspects, the driver 314 may be configured to monitor one or moreaspects of the impedance of an associated speaker 318. The impedance maybe measured so as to establish a substantially DC impedance (e.g., thespeaker impedance as measured in subsonic spectrum) measurement of thespeaker, which may be at least partially indicative of a characteristictemperature of the speaker coil. The impedance may be measured incombination with a current sensing resistor, in combination with ameasurement of the voltage applied to the speaker.

In aspects, pertaining to a driver 314 implementation with a class-Damplifier, the speaker impedance may be calculated from the outputcurrent of the class-D amplifier. The current may be pulsed along withthe ON-OFF cycles associated with the amplifier. Thus, a relevantcurrent signal may be obtained by low pass filtering the output current.The filter may be configured so as to obtain one or more spectralcomponents of the current signal. In one non-limiting example, theimpedance spectrum may be assessed in order to determine the frequencyof the first resonant mode of the speaker, and/or the impedance at thepeak of the first resonant frequency. As the impedance or associatedfrequency of the first resonant peak may change in association with theexcursion of the coil and/or the temperature of the coil. A comparisonof the impedance measured at the resonant peak with that of in thesub-sonic spectrum may be employed to extract substantially independentmeasurements of the excursion and the coil temperature during use.

The impedance of the speaker may be measured at the driver 314, for usein matching one or more control parameters, or model parameters to thephysical system of the immediate example (e.g., the impedance may beused during optimization of one or more aspects of the model 30).

In aspects, at least a portion of the observer may be configured so asto capture and/or track the first resonant peak of the speaker. Theobserver may include one or more algorithms (e.g., a frequency trackingalgorithm based on an unscented Kalman filter, AUKF, etc.) configured toextract the first resonant peak from one or more aspects of the controlsignal 15 and/or the feedback signal 25. Additionally, alternatively, orin combination, the algorithm may be configured to calculate a speakerimpedance parameter at the fundamental resonant peak. Such an algorithmmay be advantageous for performing such frequency extraction and/orimpedance measurement in real-time amongst a general audio stream (e.g.,during streaming of music, voice, etc.). With such informationavailable, one or more controllers in the nonlinear control system maybe configured to compensate for the resonant peak during operation. Suchaction may be advantageous to dramatically increase drive capability ofthe associated speaker without the need to impart mechanically dampedsolutions to the problem (e.g., by directly compensating, a highefficiency solution may be attained).

The audio system 20 may include one or more microphones 324, 326configured to monitor one or more aspects of the audio signal 321 duringuse. One or more of the microphones may be hardwired to the system 323(e.g., a microphone located on the associated consumer electronicsdevice). Such a microphone 324 may be advantageous for capturing one ormore aspects of the sound propagation in the vicinity of the speaker,associated with the speaker enclosure, the device body, etc.

In aspects, the audio system 20 may include or be coupled to awirelessly connected microphone 326 (e.g., connected via a wireless link325, 328, 330, 327), which may be connected to an associated consumerelectronics device, in the vicinity of the control system, on amanufacturing configuration (as part of a manufacturing-basedcalibration system, etc.). The wirelessly connected microphone 326 maybe advantageous for capturing one or more aspects of sound propagationin the environment around the speaker, with directional aspects of soundpropagation from the speaker, etc.

In aspects, the audio system 20 may include a speaker 318. In anothernon-limiting example, the audio system 20 may include a driver 314 and aspeaker 318.

The audio system 20 may include one or more device sensors 322 which maybe configured to capture one or more ambient and/or kinematic aspects ofthe usage environment, orientation with respect to a user (e.g.,handheld, held to the head, etc.) and provide such sensor feedback 329to one or more components of the system. Some non-limiting examples ofsuitable device sensors 322 include ambient temperature sensors,pressure sensors, humidity sensors, magnetometers, proximity sensors,etc. In aspects, the ambient temperature may be measured by atemperature sensor (e.g., a device sensor 322). Sensory feedback 329from, for example, ambient temperature may be employed by one or morecomponents in the control system as part of a protection algorithm, asinput to one or more aspects of a thermal model, etc.

The audio system 20 may include a feedback coordinator 320 configured toaccept signals from one or more components of the audio system 20 (e.g.,driver 314, transducer module 332, circuit 316, transducer 318,microphones 324, 326, device sensors 322) and generate one or morefeedback signals 25. The feedback coordinator 320 may include one ormore signal conditioning algorithms, sensor fusion algorithms,algorithms for generating one or metrics from one or more sensorsignals, extracting one or more spectral components from the signals,etc.

FIG. 3c shows a model 30 a in accordance with the present disclosure.The model 30 a includes a state estimator 336 in accordance with thepresent disclosure and optionally an output estimator 334. The stateestimator 336 may be configured to accept one or more control signals 15and generate one or more state vectors 35. The output estimator 334 mayaccept one or more states 35 and generate one or more reference signals302. The reference signals 302 may be produced for purposes ofcomparison by one or more controllers in the control system, forfeedback to a protection system, etc. The output estimator 334 mayinclude a transfer function, a nonlinear transfer function, a statebased estimator, etc. In aspects, the model 30 a may be processed in ablock based manner (e.g., simultaneously calculating output samples fromgroups of input samples), suitable for implementation in a callbackbased service (e.g., on a smartphone operating system, etc.). Such asystem may be advantageous to predict future states of the speakerswithout the need for intense sample-to-sample computational efforts.

FIG. 3d shows a model 30 b in accordance with the present disclosure.The model 30 b includes an observer 340 in accordance with the presentdisclosure and optionally an output estimator 338. The observer 340 maybe configured to accept one or more control signals 215, and one or morefeedback signals 225, and generate one or more state vectors 235. Theoutput estimator 338 may accept one or more states 235 and generate oneor more reference signals 255. The reference signals 255 may be producedfor purposes of comparison by one or more controllers in the controlsystem, for feedback to a protection system, etc. The output estimator338 may include a transfer function, a nonlinear transfer function, astate based estimator, etc.

In aspects, the observer 340 may include an augmented unscented Kalmanfilter for extracting the states from the control signals 215 and thefeedback signals 225.

FIG. 3e shows aspects of a feedback controller 342 in accordance withthe present disclosure. The feedback controller 342 includes a controlblock 344 (e.g., a nonlinear control law, a PID controller, etc.) inaccordance with the present disclosure, and optionally a signalconditioner 346. The feedback controller 305 may be configured to acceptone or more feedback signals 225 and compare the feedback signals 225 orsignals generated therefrom (e.g., a conditioned feedback signal 345)with one or more reference signals 255 (e.g., as generated by one ormore components in the control system). The compared signal is providedto the control block 344 where suitable gain is added to the signal toforce the feedback signal 225 towards the reference signal 255. Theresulting control signal 347 may be added to the initial control signal215 (e.g., as produced by one or more control components of the controlsystem) to produce a modified control signal 245 in accordance with thepresent disclosure.

FIG. 4 shows a schematic of aspects of an adaptive nonlinear controlsystem in accordance with the present disclosure. The adaptive nonlinearcontrol system includes a controller 10 b according to the presentdisclosure configured to accept one or more signals 1 and one or morestates 35 b or signals generated therefrom. The adaptive nonlinearcontrol system includes a model 30 c in accordance with the presentdisclosure. The model 30 c may be configured to accept one or controlsignals 15 b, one or more feedback signals 25 b, and/or one or moreadapted parameters 417. The model 30 c may include a model and/orobserver including one or more weighting parameters, parametricparameters, coefficients, or the like. The parameters may be storedlocally in a memory block 430, or otherwise integrated into thestructure of the model 30 c. The parameters may be at least partiallydependent upon the adapted parameters 417. The adaptive nonlinearcontrol system includes an adaptive block 410 configured to accept oneor more feedback signals 25 b, one or more control signals 15 b, one ormore input signals 1, one or more states 35 b, each in accordance withthe present disclosure, and generate one or more of the adaptedparameters 417.

The adaptive block 410 may be configured to alter the adapted parameters417 during predetermined tests, during casual operation of the nonlinearcontrol system, at predetermined times during media streaming, as one ormore components of the operating system change, as operating conditionschange, as one or more key operational aspects (e.g., operatingtemperature) changes, etc. The adaptive block 410 may include one ormore aspects configured to assess the “goodness of fit” of the currentmodel 30 c. Upon determination that the fit is insufficient, theadaptive block 410 may perform one or more operations to correct themodel 30 c accordingly (e.g., adjust a model parameter, select a modeland/or parameters or coefficients from a model class, enable one or moremodels, load one or more models, etc.).

The adaptive block 410 may include one or more adaptive and/or learningalgorithms. In aspects, the adaptive algorithm may include an augmentedunscented Kalman filter. In aspects, a least squares optimizationalgorithm may be implemented to iteratively update the adaptedparameters 417 between tests, as operating conditions change, as one ormore key operational aspects (e.g., operating temperature) changes, etc.Other, non-limiting examples of optimization techniques and/or learningalgorithms include non-linear least squares, L2 norm, averagedone-dependence estimators (AODE), Kalman filters, unscented Kalmanfilters, Markov models, back propagation artificial neural networks,Bayesian networks, basis functions, support vector machines, k-nearestneighbors algorithms, case-based reasoning, decision trees, Gaussianprocess regression, information fuzzy networks, regression analysis,self-organizing maps, logistic regression, time series models such asauto regression models, moving average models, autoregressive integratedmoving average models, classification and regression trees, multivariateadaptive regression splines, and the like.

In aspects, the adaptive nonlinear control system may include or becoupled to a power management system 405. The power management system405 may be configured to deliver a power constraint 407 to thecontroller 10 b, representative of a power level within which thecontroller 10 b must operate during use. In aspects, the model 30 cand/or controller 10 b may be configured to generate one or more powerpredictions 409 for comparison with the power constraint 407, for use inthrottling the controller 10 b in aspects where near-term powerrequirements may exceed available resource levels. In aspects, the powerprediction 409 may be delivered to the power manager 405 during use,where the power manager is configured to adjust system level powercommitments based at least in part on the power prediction 409.

FIGS. 5a-e show spectra related to acoustic coupling of a range ofhandsets to the ear of subjects in accordance with the presentdisclosure. FIG. 5a shows spectra for a handset held against an ear withdifferent acoustic coupling properties (associated with the degree ofseal against the ear, the ear properties, etc.). FIG. 5a shows a lowleakage condition 501, a low leakage 503, a high leakage 505, and a lowcoupling 507 condition (as obtained with a HATS based test rig). As canbe seen, the coupling can vary wildly during use depending on how thehandset is held against the ear. A control system in accordance with thepresent disclosure may be advantageous for compensating for suchacoustic deficiencies in the audio stream, so as to alter acoustictransfer to the ear of the subject under the resulting acousticconfiguration (i.e. adjustment based upon the state of the acousticcoupling any time during use, etc.). In aspects, a feedback signal inaccordance with the present disclosure may be related to the acousticcoupling 501, 503, 505, 507 at a particular moment in the use of thehandset, the feedback signal coupled to the control system, the controlsystem configured so as to adjust the audio stream according to theacoustic coupling (i.e. via the related feedback signal).

FIG. 5b shows relative coupling for another handset under differentcoupling conditions to an ear. Spectrum are shown for a near sealedcondition 511, low leakage condition 513, high leakage condition 515,and a variable coupling condition 517 (as often observed with a HATStest rig). The low frequency variation between coupling scenarios may beover 10 dB. In addition, the injection of background noise into thesignal may be appreciable when the leakage is relatively high duringuse.

FIG. 5c shows relative coupling for yet another handset under differentcoupling conditions to an ear as taken with a high impedance source(i.e. such as may be introduced herein via a diagnostic speaker, adriven microphone, or the like). Spectrum are shown for a near sealedcondition 519, low leakage condition 521, and a high leakage condition523. The low frequency variation between coupling scenarios vary by upto 20 dB. In addition, the injection of background noise into the signalmay be appreciable when the leakage is relatively high during use. Useof a control system in accordance with the present disclosure may beadvantageous to measure and/or compensate for such variations duringuse.

FIG. 5d illustrates the input acoustic impedance spectrum 525 to an earunder a high leakage condition to an ear. The low frequency responseshows a large variation of impedance with frequency, increasing atapproximately 10 dB per octave. Such variation is much higher than seenunder near sealed conditions, where the variation may be much lower thanin the high leakage condition. In addition, the injection of backgroundnoise into the signal may be appreciable when the leakage is relativelyhigh during use.

FIG. 5e shows frequency sensitivity response for a headset against anear under open 527 and closed 529 ear conditions. The variations in suchconditions may be related to a feedback signal in accordance with thepresent disclosure, delivered to an adaptive controller in accordancewith the present disclosure, so as to compensate on an acoustic signaldelivered to the ear during use.

In aspects, a feedback signal in accordance with the present disclosuremay be related to the acoustic coupling at a particular moment in theuse of the handset, the feedback signal coupled to the control system,the control system configured so as to adjust the audio stream accordingto the acoustic coupling (i.e. via the related feedback signal).

FIG. 6 shows a range of acoustic transfer functions 640, 650, 660between a handset transducer and the ear of a subject in accordance withthe present disclosure. The data set shown illustrates the changes inthe acoustic transfer function between a typically mounted handset (allsubstantially high leakage conditions) and an ear. Such variation, evenunder high leakage conditions may significantly affect the acousticquality perceived by the subject, the acoustic clarity, the extent ofbackground noise pickup, and the like during use. A fully sealed gasketcondition (i.e. a perfect seal between the transducer and a particularear of a particular subject) would have a flatter low frequency response(as illustrated in FIG. 5). A high leakage condition (i.e. where thereis substantially no effective seal between the transducer and the ear ofa particular subject) will such large frequency variation and lowercoupling in the low frequency spectrum. The transducer impedancespectrum during use (such as estimated by a controller in accordancewith the present disclosure) may be mapped to a corresponding acousticimpedance profile (e.g. during development, through post market studies,via feedback obtained from one or more microphones on the correspondingdevice, by playing and recording an audio stream from one or moreconfigurable receivers on the device, etc.). The map may be stored onthe device, parametrically stored on the device, included in acorresponding model bank on the device or an associated cloud storagespace, etc. Once the map is substantially established, or updated duringuse, the impedance readings may be fed to a corresponding adaptivealgorithm and/or controller in accordance with the present disclosure tocreate an enhanced audio signal so as to improve the acoustic qualityfor the subject during use.

Some spectral characteristics may change in addition to the couplingamplitude during use. Some characteristics such as the first resonantfrequency 610 of the speaker and local acoustic environment, mayincrease 620 or decrease 630 depending on the coupling and environmentalinfluence on the speaker loading (i.e. such as the proximity of thenearby surface, the structure of the surface, and the like contribute tothe ear/speaker coupling, and the acoustic loading on the speaker).

FIG. 7 shows the output of a method for fitting aspects of a nonlinearmodel in accordance with the present disclosure. The graph demonstratesan experimentally obtained signal impedance spectral response 701obtained via a method in accordance with the present disclosure or anyother known method, e.g., by mapping current and voltage measurements ofany stimuli signal in different frequency regions over time by applyinga moving band-pass filter or the like (shown as the dotted signal on thegraph) with the corresponding device held against the ear of a subject,a simulator, etc. In aspects, a “map” of impedance versus position maybe created by moving the device in the vicinity of the ear, makingreadings, and recording current/voltage to generate associated impedancespectra. In aspects, one or more additional transducers, including aproximity sensor, an imaging sensor, a microphone, a secondary speaker,etc. may be incorporated into the assessment so as to provide furtherstate related information as part of the impedance characterizationand/or mapping process. In aspects, a nonlinear state estimatorassociated with the speaker under test may be parametrically configuredwith an initial guess, this resulted in an initial approximate impedancespectrum 702. The nonlinear state estimator or nonlinear model may thenbe optimized based upon the measured spectral response 701. Theoptimized spectral response 703 is shown in the figure. As can be seen,the impedance spectrum of the speaker was a useful input for optimizingthe associated nonlinear model aspects of the nonlinear control system.

Based upon this approach, a method for optimizing a nonlinear modelincludes extracting the impedance spectrum of the speaker duringoperation (e.g., during a test, during playback of a media stream, atdifferent positions around the ear, at different gasket levels, etc.).The impedance data may be used as a target to optimize one or moreparameters of the associated nonlinear model. The resulting modelparameters may be uploaded to the model after completion, or adjusteddirectly on the model during the optimization process. Once optimized,the system may (i.e. during use) rely upon the electrical impedancereadings/estimates from the transducer in order to predict whichcontroller transfer function to implement at a given time (i.e. so as tooptimize the audio quality for the user, prevent hearing damage for theuser, etc.).

In some cases, insufficient spectral content may be available in thegeneral media stream. In these cases, audio watermarks may be added tothe media stream to discreetly increase the spectral content and thusachieve the desired optimization (e.g., white noise, near white noise,noise shaped watermarks, etc. may be added). In aspects, the audiowatermark may be transmitted from a separate transducer on the device,the first transducer (e.g. transducer nearest to the ear), configured soas to measure the audio watermark, the relationship used to furtherenhance the estimate on the state of gasket formation with a nearby ear.

FIGS. 8a-b show aspects of nonlinear hysteresis models in accordancewith the present disclosure. Large signal operation of transducers inaccordance with the present disclosure may exhibit more complicatednonlinearities than considered previously. FIG. 8a shows aspects ofinternal hysteresis loops associated with movement of a piezoelectrictransducer during operation. FIG. 8b shows an example of hysteresisloops associated with magnetization of a magnetic field duringoperation. Such hysteretic effects may be temperature and agingdependent, as well as humidity dependent. Such effects are often relatedto inefficiency, complex distortion, etc. To compensate for sucheffects, the nonlinear system may include one or more higher ordernonlinear hysteresis models. Some non-limiting examples of such modelsinclude Preisach models, Lipshin models, Bouc-Wen models, neuralnetworks, fuzzy logic models, and the like. The models may be configuredwith sufficient complexity so as to capture the necessary dynamicswithout over-complicating the computational aspects of the nonlinearcontrol system. Such models may include thermal dependencies, ratedependencies (as opposed to being rate independent), etc.

In aspects, a nonlinear control system in accordance with the presentdisclosure may include a modified Bouc-Wen hysteresis model configuredto compensate for the viscoelastic behavior of the suspension of thetransducer included in the associated CED.

In aspects, a near time invariant Preisach model may be included intothe speaker model to capture loop hysteresis and nonlinearities in oneor more nonlinear compensation blocks. The model may include temperaturevariation aspects thereof to further improve the model reliability andrange of application.

FIG. 9 shows a consumer electronics device 909 for use with a controlsystem in accordance with the present disclosure. The consumerelectronic device 909 (e.g., a smartphone handset) may be configured toproduce an audio output signal 911. The CED 909 may include anintegrated nonlinear control system in accordance with the presentdisclosure, a microphone 912, one or more proximity sensors 913, animaging sensor 914, etc. The CED 909 may be tested to determine anassociated acoustic signature during the design process, themanufacturing process, the validation process, or the like, and theaudio performance thereof adjusted through programming of the nonlinearcontrol system included therein.

Generally speaking, an observer in accordance with the presentdisclosure may be configured to operate under conditions of limitedfeedback. In such circumstances, the observer may be augmented with asuitable feed forward state estimator to assist with assessment ofstates with limited feedback.

An observer or non-linear model in accordance with the presentdisclosure may also be used to enhance robustness of a feedback system(e.g., used in parallel with a feedback controller) by providingadditional virtual sensors. In some instances, it may be the case wherea measured state may be too far off from the prediction made by theobserver or model to be realistic and therefore being rejected as afaulty measurement. In the case of detection of a faulty measurement,the observer or model generated state estimation may be used instead ofthe direct measurement until valid measurements are produced again.

The nonlinear control system may be configured with real-time impedancebased feedback, which may be over a slower time period, to provideadaptive correction and/or update of parameters in the control system,e.g., to compensate for model variations due to aging, thermal changesor the like.

The nonlinear control system may include one or more stochastic models.The stochastic models may be configured to integrate a stochasticcontrol method into the nonlinear control process. The nonlinear controlsystem may be configured so as to shape the noise as measured in thesystem. Such noise shaping may be advantageous to adjust the noise floorto a higher frequency band for more computationally efficient removalduring operation (e.g., via a simple low pass filter).

In aspects, the nonlinear control system may include a gain limitingfeature, configured so as to prevent the control signal from deviatingtoo far from the equivalent unregulated signal, so as to ensurestability thereof, limit THD, adjust an acoustic signal near a subject(e.g. such as when coupled to the ear of a subject, near the head of asubject, etc.), or the like. This gain limiting aspect may be applieddifferently to different frequencies (e.g., allow more deviation atlower frequencies and less or even zero deviation at higherfrequencies).

The state vector may be configured so as to include exact matchedphysical states such as membrane acceleration (a). In such aconfiguration, the accuracy of the position (x) and velocity (v) relatedstates may be somewhat relaxed while maintaining a high precision matchfor the acceleration (a). Thus, DC drift of the membrane may be removedfrom the control output, preventing hard limiting of the membrane duringoperation.

A control system in accordance with the present disclosure may include asimple analytical and/or black-box model of the amplifier behaviorassociated with one or more drivers. Such a model may be advantageousfor removing artifacts from the control signal that may result in driverinstability. One non-limiting example is to model an AC amplifier as ahigh-pass filter with its corresponding cut-off frequency and filterslope.

In aspects, the control system may include one or more “on-line”optimization algorithms. The optimization algorithm may be configured tocontinuously update one or more model parameters, which may occur duringgeneral media streaming. Such a configuration may be advantageous forreducing the effects of model faults over time while the system is inoperation. In a laboratory and/or production setting, the optimizationalgorithm may afford additional state feedback from an associatedkinematic sensor (e.g., laser displacement measurements of the conemovement) to more accurately fine tune the associated nonlinear modelaspects of the system (e.g., feed-forward model parameters, observerparameters such as covariance matrices, PID parameters and the like).This approach may be advantageous to apply to a tuning rig duringmanufacture of one or more CEDs including a nonlinear control system inaccordance with the present disclosure. The system may be optimizedwhile measuring as many states as practical. The associatedmulti-parameter optimization scheme may be configured to optimize to aminimum for the THD within the requested frequency range (e.g., forfundamentals up to 200 Hz).

In aspects, an optimally configured model (e.g., configured duringproduction), may be augmented with a parametrically adjustable model(e.g., a post-production adaptive control system). During the lifetimeof the associated device, the parametrically adjustable model may beadaptively updated around the optimally configured model to maintainideal operational characteristics. This configuration may beadvantageous for improving the optimization results during the lifetimeof the device, adaptively mapping the model parameters while knowing allstates (e.g., by laser, accelerometers, a sensor in accordance with thepresent disclosure, etc.) or alternatively by measuring the THD with amicrophone and optimize with that as a minimizing target and/or tosimply implement the impedance curve mapping according to any associatedmethod in accordance with the present disclosure.

The optimally configured and parametrically adjustable approach may besuitable for removing various aspects of the model that can causeinstability or bimodal response with a “black-box” representationthereof (e.g., where the input-to-output characteristics are somewhatblindly mapped).

An optimally configured and parametrically adjustable approach may beadvantageous as it may provide a means for matching an entire productline with a single adaptable model, or for matching different types ofspeakers more easily as the need for a perfect model is relaxed. Theconfiguration may be amendable to implementation with an API, laboratoryand/or manufacturing toolkit. The system may also be used tocharacterize optimally configurable (and complex) models for differentspeaker types (e.g., electro-active polymers, piezo-electric,electrostrictive and other types of electro-acoustic transducers [wherea simple model may not be a valid description of the system]) whileemploying a black box model for adaptive correction in the field (e.g.,via implementation of one or more automatic control and/or adaptationprocesses described herein).

In aspects, a model class may be suitable for implementation ofembodiments of the present disclosure. The model class may be derivedfor a class of devices and implemented in a simplified form so as toefficiently run on a processor, as part of an OS service, etc. Inaspects, a subclass of the model class may be loaded onto a respectivedevice, optionally with a plurality of such models running in parallelduring operation to predict future states of the device (e.g., predictexcursion, etc.). Such models may be used as part of a speakerprotection algorithm, as part of a control model, or the like inaccordance with the present disclosure.

In aspects, a feed-forward controller in accordance with the presentdisclosure may be assisted by a PID controller, which may be included inan associated feedback controller (to compensate for variations in thefeed forward model output). Such a configuration may be lesscomputationally intensive than alternative approaches while providing asimplified implementation. Although reference is made to PID, otherforms of control may be used, as disclosed herein.

One or more aspects of the nonlinear control system in accordance withthe present disclosure may be implemented digitally. In aspects, thenonlinear control system may be implemented in an entirely digitalfashion.

In aspects, one or more model parameters may be optimized in a labsetting, where full state feedback may be available. In such an example,a method may include determining a small-signal measurement ofequivalent Thiele-Small parameters (linear), making a rough guess to thenonlinear parameter shapes, measuring a large-signal stimuli todetermine one or more large signal characteristics, adjust the modelparameters until the output states of the model substantially match themeasured states. Such a method may be implemented using a trusted regionoptimization method, or the like. The process may also be implementediteratively with a plurality of measurements or with a range of stimuli.

In aspects, the method may include setting one or more model parameters(e.g., configuring a covariance matrix) of the controllers targetdynamics and/or inverting dynamics aspects by any known technique. Inaspects, the setting may be achieved by a brute-force approach includingtesting all possible regulator parameters within reasonable intervals tofind the settings for minimum THD. The minimum THD can then be measuredon the real system and simulated by the model and used to correct forchanges experienced by the device in the field. This approach may alsobe done iteratively while measuring the actual THD in each measurementiteration.

In aspects, the method may include configuring the PID-parameters. Suchconfiguring may be achieved by, for example, a “brute-force” approach,whereby all possible values within reasonable limits are tested whilemeasuring the THD of the speaker and searching for a minimum. In thiscase, it may be preferable to measure the THD as opposed to simulatingit.

Such a method may include measuring the impedance in accordance with thepresent disclosure. If real-time impedance measurements demonstrate aparameter mismatch severely (e.g., via severe changes in temperature orageing), the system may automatically use the new impedance curve to mapthe nonlinear model to the new system in real-time. Thus a technique forcontinuously and dynamically adapting model parameters may be providedduring system operation. Small model variations may be compensated forby a linear feedback system (e.g., a PID controller).

Such an approach may be performed in real-time. When a reliableimpedance curve may be obtained during measurement, the parameteradaptation (e.g., by trusted region optimization) may be performed. Astemperature or aging may occur relatively slowly compared with thesystem dynamics, such an adaptation approach may run occasionally,whenever the processor is “free” and does not suffer from real-timerequirements on a sample rate basis.

The nonlinear control system including an observer (e.g., an EKF, UKF,AUKF, or the like), may include an adaptive algorithm for adjusting oneor more model parameters “on-line”. The observer may then be optimizedor trained to adapt to updated model parameters while operating in thefield.

In accordance with the present disclosure, the controller may be dividedinto “Target Dynamics” (corresponding to the target behavior, e.g., alinear behavior) and “Inverse Dynamics” (which is basically aiming tocancel out all dynamics of the un-controlled system, includingnon-linearities) aspects. In this case, the target dynamics portion mayinclude one or more nonlinear effects, such as psycho-acousticnon-linearities, a compressor, or any other “target” behavior. Thus thecontroller may merge the nonlinear compensation aspects with theenhanced audio performance aspects.

A nonlinear control system may be configured to work on primarily a lowfrequency spectrum (e.g., less than 1000 Hz, less than 500 Hz, less than200 Hz, less than 80 Hz, less than 60 Hz, etc.). In one non-limitingexample, the nonlinear control system may be configured to operate on amodified input signal. In this case, the input signal may be dividedwithin the woofer band with another crossover (e.g., at 80 Hz). Themodified input signal delivered to the nonlinear control system may befocused only on the band below the crossover. Additional aspects arediscussed throughout the present disclosure.

A nonlinear control system in accordance with the present disclosure maybe embedded in an application specific integrated circuit (ASIC) or beprovided as a hardware descriptive language block (e.g., VHDL, Verilog,etc.) for integration into a system on chip (SoC), an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA), or a digital signal processor (DSP) integrated circuit.

Alternatively, additionally, or in combination, one or more aspects ofthe nonlinear control system may be soft-coded into a processor, flash,EEPROM, memory location, or the like. Such a configuration may be usedto implement the nonlinear control system at least partially insoftware, as a routine on a DSP, a processor, and ASIC, etc.

FIGS. 10a-b show spectral representations of the power 1010 andimpedance 1035 of a speaker in accordance with the present disclosure.The spectra are associated with a method for calculating a spectrum ofone or more aspects (e.g., impedance, power, voltage, current, etc.) ofa speaker in accordance with the present disclosure during operationwith a natural sound source (e.g., with a music stream, a conversation,etc.). FIG. 10a shows a power spectrum 1010 generated from a naturalaudio stream as averaged over a time period during use (e.g., asaveraged over a 100 ms period, a 250 ms period, etc.). Overlaid onto thepower spectrum is shown a threshold 1015, which may be organized basedon a predetermined threshold (e.g., a power level, a voltage, a current,an excursion, etc.), a frequency dependent threshold, etc.

In aspects, changes in the impedance and/or relative power spectra maybe an indication of the extent of an ear gasket formed when theassociated transducer is placed against the ear of a subject. In thatsense, the changing impedance and/or relative power spectra may be fedinto an adaptive algorithm, controller, observer, model bank, and/or acombination thereof so as to generate one or more parameters suitablefor adapting the controller, adjusting the audio stream renderedtherefrom, or the like, so as to optimize the audio input to the ear ofthe subject. Such a configuration may be suitable for maximizing soundclarity and/or optimizing sound level/clarity during general use.

In aspects, the threshold 1015 may be used to determine which regions ofthe spectrum 1010 may contain (for the timeframe in question) asignificant level of information, suitable for further analysis. In FIG.10 a, multiple spectral bands 1020 a-d are shown with informationpresenting at levels above the local threshold 1015. In aspects, theanalysis may include updating a model, adaptation of a parameter set,construction of a property table, etc.

FIG. 10b shows a spectral representation 1035 of an impedance model fora speaker in accordance with the present disclosure. The model may be anadaptive model, a parametric model, generated from one or more spectralband averaged parameters, etc. In the non-limiting example shown in FIG.10b , the spectrum may be split into multiple bands (e.g., 2 bands, 8bands, 16 bands, 64 bands, etc.). Within each band, a property value1030 (e.g., impedance, excursion, etc.) is measured during use. A finitenumber of property values 1030 within each band may be stored for inputto a model (e.g., an adaptive model, a curve fit, etc.) for use inpredicting the overall property spectrum 1035 of the speaker at any timeduring use thereof. Such information may be generated and/or updated asnecessary to predict one or more states of the speaker, as feedback intoa control system in accordance with the present disclosure, etc.

In aspects, a method for generating a property spectrum for a speakermay include playing an audio stream with the speaker under test,measuring current and voltage associated with the speaker (e.g., via useof a series resistor, etc.), generating one or more spectra from themeasured signals (e.g., generation of a bass band spectrum, a mid-bandspectrum, etc.), analyzing one or more of the spectra to determinefrequency bands of interest therein (e.g., frequency bands including asignificant signal level in relation to a threshold value/function), andcalculating property spectral bands in the frequency bands of interest.The method may include combining the property spectral bands withpreviously measured bands, updating a model with one or more of theproperty spectral bands, updating an adaptive model for a propertyspectrum using one or more of the property spectral bands, etc.

In aspects, the measured signals may include current through and voltageacross a speaker input (e.g., voice coil, electrodes, etc.). Theproperty may include impedance of the associated speaker, etc. Thegeneration of the spectra may be completed using an FFT, a multi-bandfilter and one or more averaging filters, etc.

FIG. 11 shows aspects of a system for generating variables from signalsmeasured from a speaker in an environment in accordance with the presentdisclosure. In aspects, the speaker may be substantially coupled withthe ear of a subject during use, the feedback pertaining to acombination of the speaker parameters, and the acoustic coupling betweenthe speaker, the ear, and the surrounding environment. The system may beconfigured to accept one or more feedback signals (e.g., current,voltage, an excursion value, etc.), and to deliver one or more of thefeedback signals to a band updater 1110. The band updater 1110 may beconfigured to generate one or more multi-band references relating to thefeedback signals (e.g., a multi-band vector, a spectrum, etc.). One ormore of the references may be made available to one or more aspects of asystem in accordance with the present disclosure, as a feedback elementto a nonlinear control system in accordance with the present disclosure,or the like. The system may include one or more property extractionblocks (e.g., functional blocks, a power tracking block 1115, atemperature tracking block 1120, a characteristic tracking block, aresonant frequency tracking block 1125, an acoustic quality trackingblock 1130, etc.), configured to analyze the updated spectrum, and togenerate one or more associated parameters therefrom. Some non-limitingexamples of property extraction blocks include a power tracking block1115, a temperature tracking block 1120, a resonant peak tracking block1125, an acoustic quality tracking block 1130, combinations thereof, andthe like.

In aspects, during operation, the update process may be configured at arate suitable for operation within a service on an operating system(e.g., as a background service on a smartphone operating system), etc.Such an adaptive process may be advantageous for minimizing hardwarerequirements of the system, providing a flexible working environment,etc.

In aspects, a power tracking block 1115 may be configured to track apower metric, from one or more of the multi-band references (e.g.,spectra), obtained from the band updater 1110 during use. The powertracker 1115 may also accept one or more parameters (e.g., resonantpeak, an acoustic quality, a temperature, an excursion spectral model,an output from an associated block 1130, etc.) as part of the analysisprocess. In aspects, the power tracker 1115 may be configured topartially calculate an excursion value for an associated speaker inaccordance with the present disclosure. In aspects, a representativepower value may be calculated by integrating the combined spectrum of acurrent and voltage signal for an associated speaker over a spectralband of interest. The integration may include combination with anadditional excursion model 1135, configured to relate the input power atone or more wavelengths to a corresponding excursion value.

In aspects, the power tracker 1115 may provide a prediction of near termupcoming power requirement for the speaker (e.g., P_(estimate)). Suchinformation may be provided to a power management service elsewhere inthe system in order to plan for resource management, soft transitionspeaker output, avoid brownout conditions, or the like.

In aspects, one or more parameter tracking block(s) and/or modelingblock(s), may accept one or more of a temperature value, excursion,acoustic impedance to a nearby surface, proximity to a nearby surface,orientation to a nearby structure, a thermal value, etc. In aspects, anassociated modeling block may include a temperature dependent model forcalculating an excursion parameter during use. In aspects, the systemmay include a peak temperature tracker 1140 configured to estimate thenear-term upcoming peak temperature on a speaker element given the inputhistory of one or more inputs (e.g., as predicted by one or morefeedback parameters in the system), which may be in combination with anambient temperature reading, etc.

In aspects, one or more of the parameter tracking block(s) and/ormodeling block(s) may accept one or more audio input values from atransducer on the device, on a separate device, in the vicinity of thesubject (i.e. cross device acoustic coupling, etc.), or the like. Inaspects, the audio feedback modeling block may be configured tocalculate an environmental signature from the feedback, the signature atleast in part pertaining to the proximity of the speaker to a nearbystructure, such as the ear of the subject. In aspects, this modelingblock may be configured to generate an ear gasket profile parameter,which may be fed into the system as an additional state, a disturbanceprediction, or the like.

In aspects, the system may include a disturbance tracker 1145,configured so as to determine if a degree of damage and/or change hasoccurred with the system (e.g., such a change in acoustic quality Q,etc.) during use. Such information may be suitable for incorporationinto a lifetime predicting algorithm, or the like.

The band updater may include an FFT, an adaptive model, or the likeconfigured to generate the updated reference from one or more of thefeedback signals.

The system may be configured to deliver one or more references, feedbacksignals, parameters, etc. to one or more aspects of a control system inaccordance with the present disclosure.

In aspects, the system may include a spectrum model 1150 configured toextract updated band information from the band updater 1110 and togenerate a continuous spectral model therefrom (e.g., such as a secondorder model, etc.). Such a model may be used by one or more systemprocesses, controllers, or the like in order to improve speakerperformance, and/or provide aspects of a speaker protection function.

FIG. 12 shows an optionally multi-rate system for generating variablesfrom signals measured from a speaker in accordance with the presentdisclosure. The system may include a multi-rate subsystem for splittingone or more of the feedback signals into one or more frequency bands foranalysis. In aspects, each band may be treated separately in order toextract suitable band information during use.

The channel updater 1210 may be configured to generate one or moremulti-channel references relating to the feedback signals (e.g., amulti-band vector, a spectrum, etc.). One or more of the references maybe made available to one or more aspects of a system in accordance withthe present disclosure, as a feedback element to a nonlinear controlsystem in accordance with the present disclosure, or the like. Thesystem may include one or more property extraction blocks (e.g.,functional blocks, a power tracking block 1215, a temperature trackingblock 1220, a characteristic tracking block, a resonant frequencytracking block 1225, an acoustic quality tracking block 1230, anexcursion tracking block 1235, a disturbance tracking block 1245, etc.),configured to analyze the updated spectrum, and to generate one or moreassociated parameters therefrom.

FIG. 13 shows a semi-logarithmic graph outlining some non-limitingexamples of relationships between stress state and cycles to failure fora speaker in accordance with the present disclosure. The graph showslogarithmic cycles to failure against a magnitude of stress for a rangeof non-limiting example speakers 1310: a low cost speaker, mid-rangespeaker, and a high performance speaker 1315.

In aspects, a relationship between cycles to failure and stress may beincorporated into one or more aspects of a speaker protection system inaccordance with the present disclosure. The remaining lifetime may beestimated using such information as part of a lifetime prognosticatingsubsystem as part of the speaker protection system. In aspects, a valuerelating to the combination of stress and application time may begenerated during use of the speaker. The value may be configured incombination with such a stress-cycle relationship to generate anestimate of the remaining lifetime of the speaker in the field.

In aspects, a usage profile for a speaker in accordance with the presentdisclosure, may be generated by integrating a stress parameter (e.g., anexcursion augmented power level, a thermal parameter, a combinationthereof, etc.) with a duration (e.g., time under stress), so as togenerate a metric which designates a quantifiable level to which thespeaker has been operated under stress during usage thereof. Such ametric may then be used to predict remaining lifetime of the speaker. Inaspects, the maximal stress levels that may be applied to the speaker inuse may be augmented in real-time while in service based on the usageprofile to date (e.g., the maximal allowed stress may be reduced basedon the amount and severity of usage of the speaker to date).

In aspects, the usage profile for a speaker in accordance with thepresent disclosure, may contain a listing of preferred ear gasketfunctions, impedance spectra during ear gasket related usage, or thelike for a user so as to form an ear gasket history. The ear gaskethistory may be applied to a model bank, etc. in order to optimize themodels stored in the bank, provide an associated deep learning algorithmwith suitable information for adapting the speaker performance to thepreferred habits of the user, etc.

FIGS. 14a-c show aspects of systems for extracting parameters from oneor more signals measured in a system in accordance with the presentdisclosure. FIG. 14a shows aspects of a system to extract one or morespectral aspects of a property (e.g., impedance, Q, f_(r), etc.), and/ora state (e.g., excursion, velocity, acceleration, current, voltage,power, etc.) during operation thereof. The system may be configured toreceive one or more signals (e.g., voltage, current, excursion, etc.) orsignals generated therefrom (e.g., band limited portions thereof, etc.).The system may include band averaging blocks 1410, 1415, configured togenerate an average magnitude within a frequency band of an associatedinput. The system may be configured to perform one or more operations1420, 1425 (e.g., arithmetic operation, multiplication, division,conversion, filter, etc.) on the average magnitudes to generate one ormore discrete frequency band estimates therefrom. The frequency bandestimates may be a computationally simplified representation of afrequency spectrum for the parameter, for use by one or more aspects ofan associated protection system, control system, model generationalgorithm, etc.

Some aspects of temporal data 1430, 1435 along with associatedband-limited spectra 1440, 1450, and a fitted impedance model 1445(e.g., a linear model, a biquad filter based model, etc.), are shown toclarify the parameter extraction process.

FIG. 14b shows aspects of a system to extract and/or predict one or morespectral aspects of a property (e.g., impedance, Z), or a state (e.g.,excursion x, power p, etc.) from one or more inputs during operationthereof. In aspects, the system may be configured to calculate a totalpower or energy estimate from one or more feedback signals (e.g.,voltage, current, excursion, etc.) or signals generated therefrom (e.g.,band limited portions thereof, etc.). The system may include bandaveraging blocks 1455 a-n, configured to generate an average magnitudewithin a frequency band of an associated input. The system may beconfigured to perform one or more operations 1460 (e.g., arithmeticoperation, conversion, filter, etc.) on the average magnitudes togenerate the associated power and/or energy estimates. Such aconfiguration may be advantageous for calculating the desired propertiesin a computationally efficient method, amendable to implementation in abackground service on an operating system.

FIG. 14c shows aspects of a system to extract one or more aspects of aproperty (e.g., impedance) or a state (e.g., excursion, power, etc.)during operation thereof. The system may include band averaging blocks1465 a-n, configured to generate an average magnitude within a frequencyband of an associated input. The system may include one or moreexcursion models 1470 configured to calculate an excursion parameterx_(estimate) from one or more feedback signals (e.g., voltage, current,excursion, etc.), one or more estimated parameters Q, T, f_(r) (e.g.,one or more model parameters, an acoustic quality, a coil temperature, aresonant frequency, an impedance model, an acoustic model, etc.) orsignals generated therefrom (e.g., band limited portions thereof, etc.).In aspects, the excursion model 1470 may be generated from physicalrelationships between displacement and impedance (e.g., from aparametric model, from a physical model, etc.), from an adaptive model,as part of a test procedure, etc. In aspects, the system may include aplurality of excursion and/or impedance models 1470 or the likeconfigured to operate simultaneously during operation, the outputthereof compared against a measured signal or characteristic todetermine and/or select the model 1470 that is most representative ofthe present state of the associated acoustic system.

In aspects, the system may be configured to compare an extracted stateand/or estimated parameters against values recently collected in freespace (such as when the device is not located near an obstruction, auser surface, an ear, etc.). The extracted state and/or estimatedparameters may be compared against recently confirmed free stateparameters, the free state difference being representative of the stateof engagement of the speaker with a nearby surface (i.e. the degree offormation of a gasket effect with a nearby surface). The free statedifference may then be used as a parametric input to the controller, toadjust the audio stream prior to rendering so as to improve the audiolevel, clarity, spectral distribution, etc. in the present environmentalstate. Some non-limiting examples include, a free state approximation(i.e. a state where the speaker is basically far from any nearbysurfaces), a restricted state (i.e. a state where the speaker is near asurface but there is still substantial acoustic leakage there around), afully sealed state (i.e. a state where the speaker is coupled with asurface so as to form a substantially complete gasket), or one or morestates in between. In aspects, the free state difference may be acontinuous parameter, used to modify in a discrete or pseudo continuousfashion, one or more controller parameters.

FIGS. 15a-c show aspects of a system for controlling a speaker 1520 inaccordance with the present disclosure. FIG. 15a shows a system forcontrolling a speaker configured to accept an input audio signal(input), including a controller 1510 in accordance with the presentdisclosure. The controller 1510 may be configured to accept the inputsignal and/or one or more feedback signals or signals generatedtherefrom and to generate one or more control signals for use by one ormore aspects of the system. The system may include an amplifier 1515configured to accept the control signal and one or more feedback signals(e.g., current, voltage, excursion, etc.), or signals generatedtherefrom (near-term predictions of states, a property, an environmentalcondition, etc.), and to generate a drive signal to drive an associatedspeaker 1520. The system may include one or more sensory feedback blocks1525, configured to measure and optionally convert one or more feedbacksignals from the speaker or audio system component. The sensory feedbackblock 1525 shown in FIG. 15a may be configured to monitor one or moreaspects of the voltage, and/or current provided to the speaker 1520, andto optionally generate one or more feedback signals therefrom (e.g.,filtered signals, band limited signals, raw signals, etc.). The systemmay include a property tracker 1530 in accordance with the presentdisclosure configured to accept one or more feedback signals or signalsgenerated therefrom, and to calculate a property (e.g., impedance,resonant frequency, cutoff frequency, nonlinear acoustic parameter,etc.) for use by one or more aspects of the system in accordance withthe present disclosure. One or more of the properties may be used aspart of a control algorithm included in the controller, a protectionalgorithm included in the controller and/or the amplifier, etc. Inaspects, the property tracker 1530 may forward one or more of thefeedback signals onto the controller 1510, and/or amplifier 1515 duringuse.

FIG. 15b shows a subsystem in accordance with the present disclosureconfigured to generate one or more property spectra from one or morefeedback signals (current, voltage, v_(i)(t), i_(i)(t), etc.), which maybe measured during general use of an associated speaker (e.g., withoutpreconceived test signals, etc.). The subsystem may include one or morethreshold blocks 1540, 1545, configured to calculate when the feedbacksignals or a portion thereof have significant content for furtheranalysis. The subsystem may include a sparse spectrum generator 1550configured to accept the significant content and generate one or moresparse spectra therefrom (e.g., portions of a complete spectrum asavailable from the significant content of the feedback signals). Thesubsystem may include a sparse data model 1555 into which sparse spectramay be incorporated as available based on the particular usage case. Thesubsystem may include one or more models, adaptive models 1560, etc. toaccept one or more aspects of the sparse spectra and/or an error signalfrom one or more of the sparse data models 1755 during use. The adaptivemodel 1560 may be configured to make a stabilized, full spectral modeltherefrom. The stabilized full spectral model may be made available toone or more aspects of the system (e.g., a control algorithm, a soundquality enhancement algorithm, an amplifier, etc.) for use in thecontrol and/or protection of the speaker. In aspects, the full spectralmodel may be added to a model bank in accordance with the presentdisclosure, as feedback for aging studies, etc.

FIG. 15c shows an impedance frequency response at a present time periodrelating to significant content 1570 (measured over particular bandswithin the spectrum), and a visual example of a full spectral model 1575fit thereto, obtained for the particular time period in question. Themodel 1575 may be updated as available from the significant content 1570from the present time period as well as significant content obtainedduring previous time periods.

FIG. 16 shows aspects of a schematic of an active speaker control system1610 in accordance with the present disclosure. In aspects, one or morecomponents of the active speaker control system 1610 may be includedinto an integrated circuit in accordance with the present disclosure.FIG. 16 shows a control system 1610 for controlling a speaker 1625configured to accept an input audio signal (e.g., communicated with anexternal processor, controller, etc., which may be part of a digitalcommunication signal, via I2S [Integrated Interchip Sound], and thelike), and a power signal (e.g., from a power source, a battery, etc.).The control system 1610 may include a communication block 1640configured to communicate one or more signals (e.g., the audio signal, aconfiguration signal, a sensory signal, a status signal, a powerrequirement, a power prediction, a power constraint, etc.) to/from anoutside source (e.g., a processor, a communication subsystem, etc.). Thecommunication block 1640 may be configured to communicate one or more ofthe signals with one or more aspects of the control system 1610. Thecontrol system 1610 may include a controller 1620 in accordance with thepresent disclosure. The controller 1620 may be configured to accept theinput signal and/or one or more feedback signals or signals generatedtherefrom and to generate one or more control signals for use by one ormore aspects of the system 1610. The system 1610 may include anamplifier (in this case, integrated into the controller) configured toaccept the control signal and one or more feedback signals or signalsgenerated therefrom and to generate a drive signal to drive anassociated speaker 1625. The system 1610 may include one or more sensoryfeedback blocks 1635, configured to measure and optionally convert oneor more feedback signals from the speaker 1625, membrane actuator, anembedded sensor, and/or one or more system components. In aspects, adrive signal sensory feedback block 1630 shown in FIG. 16 may beconfigured to monitor one or more aspects of the voltage and or currentprovided to the speaker 1625 and to generate one or more feedbacksignals therefrom (e.g., filtered signals, band limited signals, rawsignals, etc.). The system may include a sensory feedback block 1635 inaccordance with the present disclosure configured to interface with oneor more sensors and to generate one or more feedback signals or signalsgenerated therefrom for use by one or more aspects of the system 1610(e.g., by the communication block 1640, the controller 1620, forcommunication to an external system, etc.). One or more of theproperties may be used as part of a control algorithm included in thecontroller 1620, a protection algorithm included in the controller 1620,and/or the amplifier, etc.

FIGS. 17a-b show aspects of methods for updating an adaptive model inaccordance with the present disclosure. FIG. 17a shows aspects of amethod including playing an audio stream 1710 with the speaker undertest, measuring one or more sensory signals associated with the speaker1715 (e.g., via use of a series resistor, a coulomb counting sensor, acharge monitor, a voltage sensor, a sensor, etc.), or a nearby acousticcoupling (e.g. such as an acoustic load on the speaker, a leakageparameter between the speaker and a nearby surface, an acoustic couplingto a surface, a lack of coupling between the speaker and an externallyplaced microphone etc.), generating one or more spectra from themeasured signals 1720 (e.g., generation of a bass band spectrum, amid-band spectrum, etc.), analyzing one or more of the spectra todetermine frequency bands of interest therein (e.g., frequency bandsincluding a significant signal level in relation to a thresholdvalue/function), and updating an adaptive model 1725 using one or moreof the analyzed spectra.

In aspects, the measured signals may include current through, chargeaccumulation on, and/or voltage across a speaker. The property mayinclude impedance of the associated speaker, etc. The generation of thespectra may be completed using an FFT, a multi-band filter and one ormore averaging filters, etc.

FIG. 17b shows aspects of a decision making method to determine theimmediate adaptation rates associated with the update process for anadaptive model in accordance with the present disclosure. The decisionmaking method may include collecting data 1730, updating the model at afirst rate 1735, assessing any changes in the model, and if asignificant change is determined, perform an accelerated test 1740. Sucha configuration may be advantageous for assessing dramatic changes in aspeaker or an environment surrounding the speaker (e.g., placement of afinger over a speaker vent, etc.), so as to rapidly respond to thosechanges, so as to prevent short term damage to the speaker during use.In aspects, the accelerated test 1740 may include adding (e.g.,superimposing) a test signal over top of the audio stream so as toguarantee that significant content will be generated in the spectralbands of interest as part of the assessment and adaptation process. Inaspects, the accelerated test 1740 may include changing thresholdlevels, averaging times and the like in the sensor data processingalgorithms in order to get less exact but quicker adaptive behavior.

FIG. 18 shows aspects of a method for calculating one or more parametersfrom spectra in accordance with the present disclosure. The methodincludes calculating an approximate frequency f_(r) associated with thepeak of an impedance spectrum 1810, excursion spectrum, etc. FIG. 18shows an associated frequency response as measured at bands (f₁-f₇) overthe frequency spectrum of interest. The individual band measurements areused as a weighted sum to calculate the weighted average of thefrequency response. The weighted average may be used to calculate areference frequency associated with the distribution of the spectrum,which may change with temperature, environment, etc. Such a referencefrequency may be advantageous for inferring a change in temperatureand/or environment during use of the speaker in the field. In aspects,such a simplified method may be adapted to estimate the acoustic qualityQ, and/or the bandwidth of a resonant peak of interest during use. Inaspects, the acoustic quality may be estimated from the peak impedanceat the resonant peak f_(r) compared against the DC or near DC impedancein the spectrum (in practice that value may be obtained by measuring theimpedance over the mid/high non-resonant frequency region of thespectrum, typically around 3000-5000 Hz for an electromagneticmicrospeaker).

FIGS. 19a-g show aspects of techniques and relationships for derivingone or more speaker parameters and/or predicting the remaining lifetimeof a speaker in accordance with the present disclosure. FIG. 19a showsaspects of an impedance spectrum for a speaker as measured at lowtemperature 1914 and at high temperature 1912 during use. In aspects, anactive speaker in accordance with the present disclosure may include athermal sensor (e.g., a non-contact thermal sensor) to determine thetemperature profile of a membrane actuator, voice coil, magnet, etc.during use. Such information may be combined with impedance readings tobetter select, enable use of, and/or adapt a model for use in one ormore aspects of the system (e.g., a controller, a property tracker,etc.).

FIG. 19b shows aspects of an accumulated usage model, configured toestimate the weighted usage value 1922 to date, and/or remaininglifetime for a speaker unit during use. The model may include a “stress”variable combined with a temporal component (e.g., so as to derive astress−time factor relating to usage of the speaker). The stress−timefactor may then be integrated (e.g., leaky integrated) over time inorder to form the accumulated weighted usage value 1922. In aspects, theresulting information may be used to determine periods of inactivity1920 as well as periods of excessive use, or the like.

FIG. 19c shows aspects of a model for stress variables (e.g., ageaccelerating factors) for a speaker. The Figure shows a thermalacceleration factor 1927 and an excursion acceleration factor 1929,which both monotonically increase towards a critical level 1925 beyondwhich damage may be immanent. Such values may be advantageous forcalculating a weighted average of usage for an associated speaker duringuse.

FIG. 19d shows aspects of an alternative thermal lifetime curve for aspeaker, outlining the relationship between cycles to failure and theoperating temperature during use. The curve 1930 may be a master curvegenerated for a population of speakers during a manufacturing process,field testing study, etc. In aspects, the curve may be compared againstthe running average temperature to date associated with the speaker toestimate the remaining lifetime thereof. Some aspects of the peakallowable operating temperature 1932, the maximum temperature duringtransient operation 1934, and the average running temperature 1938 arehighlighted for reference.

FIG. 19e shows aspects of a graphical relationship used to interrelateimpedance 1940, 1942 measured at different excursion levels, related totemperature for a speaker in accordance with the present disclosure.From an estimate of either two of the values, such an LUT may be used toestimate the 3^(rd) value of the triad.

FIG. 19f shows aspects of age-related stress on a speaker. FIG. 19fdemonstrates a range of stress/time trajectories for “normal” operationof a speaker in a family under a low temperature 1950 and a hightemperature 1952 operating condition. FIG. 19f also illustrates a stresscurve measured estimated for a particular sample device 1954 includingan over stress event (e.g., a period of over excursion, physical impact,or increased temperature) that lead to a recoverable aging predictionfor the system.

FIG. 19g shows aspects of an aging curve 1964 superimposed on agraphical representation of a frequency/acoustic quality model for aspeaker obtained at different operating temperatures 1960, 1962. Inaspects, the trajectory of the aging curve, as measured in the spaceassociated with the speaker properties and environmental conditions, maybe used to determine if a particular speaker may be aging in apredictable manner, or if an event has altered the aging trajectory forthe particular speaker.

FIG. 20 shows a schematic of aspects of a speaker protection system inaccordance with the present disclosure. In aspects, such a speakerprotection system may be coupled with a control system for adjusting anaudio stream. In aspects, the speaker protection system may beconfigured to generate one or more parameters (e.g. an excursionparameter, an impedance parameter, a proximity model, etc.) which may beused internally for speaker protection, but may also be provided to thecontrol system for use in adjusting an audio stream to compensate forcoupling with a nearby object. In one non-limiting example, an excursionstate of the speaker may be used as input to a feedback algorithm forthe control system. The excursion state and an acoustic output may beused to estimate an acoustic transfer function, an acoustic load on thespeaker, etc. Such a transfer function, load, etc. may be related to aproximity state of interest between the speaker and a nearby object.

The speaker protection system shown includes an estimator 2010 inaccordance with the present disclosure, configured to accept an inputsignal 2001 and optionally a feedback signal 2004 and/or a postcompressed signal 2035 and to produce an estimation signal 2015. Theestimation signal 2015 may be representative of a speaker parameter(e.g., voice coil excursion, a sound pressure level, a chamber pressure,etc.). In aspects, the estimator 2010 may be configured to produce theestimation signal 2015 without any form of feedback (e.g., without theoptional feedback signal 2004 or the post compressed signal 2035). Inaspects, the estimator(s) 2010 may be implemented in a purely feedforward configuration. Such an implementation may be advantageous forintegration into a background service as provided to an operatingsystem, etc.

In aspects, the speaker protection system may include a protection block2030 configured to accept the input signal 2001 or a signal generatedtherefrom (e.g., such as a delayed input signal 2025), and theestimation signal 2015, and to produce an output signal 2003 fordelivery to a speaker, a driver circuit, or the like. In aspects, theprotection block 2030 may be configured to accept a kinetic and/orkinematic feedback signal 2045 (e.g., an accelerometer output, gyrometeroutput, acceleration based interrupt, etc.) for use in generating theoutput signal 2003. In aspects, the kinetic and/or kinematic feedbacksignal 2045 may be an event driven interrupt (e.g., a binary signalrelating to an event such as free fall, an impact, a maximum rotationrate, a rapid change in ambient conditions, a rapid change in altitude,etc.). In aspects, the protection block 2030 may be configured to limitthe delayed input signal 2025 based upon one or more of the estimationsignal 2015, the kinetic and/or kinematic feedback signal 2045, or thelike.

In aspects, the post compressed signal 2035 may be compared with thefeedback signal 2004, the input signal 2001, the delayed input signal2025, or the like in order to estimate a speaker parameter, adjust oneor more estimation models, etc.

In aspects, the post compressed signal 2035 may be optionally used forfeedback to an iterative prediction process. In aspects, such a signalmay be connected to a matching compression block, ahead of the delayblock 2020. Such a configuration may be advantageous for maintaining thefeedback signal 2035 as part of a real-time prediction algorithm (e.g.,using delays to keep blocks within the system working on the sametime-stamped data).

In aspects, the estimator(s) 2010 may be configured to produce a powerprediction 2006 in accordance with the present disclosure. The powerprediction 2006 may be produced in parallel with the estimation signal2015 (e.g., in parallel with an estimate for upcoming excursion, etc.).Such a power prediction 2006 may be advantageous for overcoming brownoutconcerns, compared with a power limit, etc. as part of a compressionprocess, etc.

FIGS. 21a-e show aspects of excursion estimators each in accordance withthe present disclosure. FIG. 21a shows aspects of an estimator 2110 inaccordance with the present disclosure, configured so as to accept aninput signal 2101 and to generate an estimation signal 2115. Theestimator 2110 includes one or more estimating models 2111, 2112, 2113,each configured to generate an estimate from the input signal 2101. Inaspects, the estimating models 2111, 2112, 2113 may be linear smallsignal models configured to generate an estimate/prediction of a speakerstate (e.g., such as excursion, acceleration, power consumption, etc.)without significant computational requirements. In aspects, one or moreof the estimating models 2111, 2112, 2113 may be derived from a modelclass described herein. In aspects, one or more of the estimating models2111, 2112, 2113 may be configured so as to estimate the speaker stateas characterized during manufacturing testing of a family of devices(e.g., from sampled data taken from manufacturing lot data, from virtualtest data, etc.). In aspects, one or more of the estimating models 2111,2112, 2113 may be an adaptive model in accordance with the presentdisclosure.

In aspects, the estimator 2110 may include a selector 2114 configured toaccept one or more outputs from the estimating models 2111, 2112, 2113and to generate the estimation signal 2115 therefrom. In aspects, theselector 2114 may be configured to select the worst case output from theestimating models 2111, 2112, 2113 for use in the estimation signal 2115(e.g., selecting output from one or more of the models to represent theestimation signal 2115). In aspects, the selector 2114 may be configuredso as to output a function of the estimating model 2111, 2112, 2113outputs (e.g., a linear combination, a weighted sum, a sum of absolutevalues thereof, etc.). In aspects, the selector 2114 may be configuredto enable one or more models 2111, 2112, 2113 deemed to be mostappropriate based upon a selection criteria (e.g., comparison tohistorical data, comparison with feedback or signals/characteristicsobtained therefrom, comparison with device family histories, a higherorder interpolation, etc.).

In aspects, the selector 2114 may be configured to accept a feedbacksignal 2104 (e.g., a measured current, impedance, voltage, excursion,etc.) to compare against one or more model outputs 2111, 2112, 2113and/or co-processed characteristics (e.g., model processed current,impedance, voltage, excursion, power, etc. calculated in a model pairwith each of the models 2111, 2112, 2113, etc.) so as to validate theselection process, to initiate a test, as feedback to a model adaptationprocess, or the like.

In aspects, the selector 2114 may be configured to enable or disableoperation of one or more of the models 2111, 2112, 2113 (and optionallystoring, for further testing, co-processed characteristics, such as,without limitation, model processed current) as part of the selectionprocess. Such a configuration may be advantageous for reducingcomputational power while maintaining a high quality of protection forthe associated speaker.

FIG. 21b shows aspects of an estimator 2120 in accordance with thepresent disclosure. The estimator 2120 is configured to accept an inputsignal 2101 or a signal generated therefrom and to produce an estimatingsignal 2115 b. The estimator 2120 may be configured to accept one ormore parameters 2124 (e.g., model parameters, filter coefficients,etc.), which may be loaded into the estimator from a model bank 2122.The model bank 2122 may include a plurality of models (e.g., parametricmodel parameters, filter coefficients, etc.) representative of thedevice in question. The loading process may be initiated by a testperformed in accordance with the present disclosure. In aspects, such atest may be performed on the device (e.g., in combination with one ormore forms of feedback). Alternatively, additionally, or in combinationone or more aspects of the test may be performed remotely from thedevice (e.g., on a server, in a data center, in the cloud, at a testkiosk, etc.). In aspects, the model from the model bank may be selectedvia a feedback based comparison with one or more model characteristicsand a characteristic of the device measured (e.g., derived fromfeedback) during operation in accordance with the present disclosure.

In aspects, the estimator 2120 may be configured to produce a powerprediction 2106 in accordance with the present disclosure.

In aspects, the estimator 2120 may be configured to accept a feedbacksignal 2104 (e.g., a measured current, impedance, voltage, excursion,etc.) to compare against one or more estimated signals internal to theestimator 2120, and/or co-processed characteristics (e.g., modelprocessed current, impedance, voltage, excursion, power, etc.) so as tovalidate the estimated output 2115 b, to initiate a test, as feedback toa model adaptation process, or the like.

FIG. 21c shows aspects of an estimator 2130 in accordance with thepresent disclosure. The estimator 2130 may be configured to accept aninput signal 2101 or a signal generated therefrom and to produce anestimating signal 2115 c. The estimator 2130 may be configured to acceptone or more parameters 2129 (e.g., model parameters, filtercoefficients, etc.), which may be loaded into the estimator from a modelbank 2127. The model bank 2127 may include a plurality of models (e.g.,parametric model parameters, filter coefficients, etc.) representativeof the device in question and optionally one or more modelcharacteristics (e.g., impedance parameters, resonant frequency,acoustic quality, frequency response plots, etc.), which may be used todetermine which model most closely fits a test measurement withoutrequiring significant computational load.

The system may include a testing function 2125, configured to accept oneor more feedback signals 2104, optionally in real-time, and/oroptionally an input signal 2101 or a signal generated therefrom, inorder to derive one or more measured characteristics, and compare themwith one or more model characteristics 2128 to determine the nearestfitting model (or group of models). In aspects, the testing function2125 may generate a selection signal 2126, an enable vector, a weightingfunction, etc. which may be used to select, enable, weight, update,and/or to generate a model from the model bank 2127 for loading into theestimator 2130, for enabling use thereof, or for use in conjunction withthe estimator 2130. In aspects, the model characteristics may becompared to corresponding characteristics associated with the modelsincluded in the model bank 2127, so as to facilitate selection of themodel(s) most closely representing the characteristic in question. Suchmodel(s), model parameters, etc. may be loaded, activated, or the likein order to interact with the estimator 2130 processes.

In aspects, the estimator 2130 may run in parallel with any testingfunction 2125, etc. The loading/weighting process 2129 may be configuredto include a transitional period whereby the updated model and/orweighting changes are slowly introduced so as to minimize the chance ofaudible transitions, over excursion events, etc. during the estimatorupdate.

In aspects, the estimator 2130 may be configured as an observer inaccordance with the present disclosure. In aspects, the observer mayinclude an EKF, UKF configuration as described herein.

FIG. 21d shows aspects of an estimator 2140 in accordance with thepresent disclosure. The estimator 2140 may be configured to accept aninput signal 2101 or a signal generated therefrom and to produce anestimating signal 2115 d. The estimator 2140 may be configured to acceptone or more parameters 2143 (e.g., model parameters, filtercoefficients, weighting functions, etc.), which may be loaded into theestimator from a model bank 2139. The model bank 2139 may include aplurality of models (e.g., parametric model parameters, filtercoefficients, etc.) representative of the device in question andoptionally one or more model characteristics (e.g., impedanceparameters, resonant frequency, acoustic quality, frequency responseplots, etc.), which may be used to determine which model most closelyfits a test measurement without requiring significant computationalload.

In FIG. 21d , the input signal 2101 and/or feedback signals 2104 may beloaded into storage 2135, so as to form a signal history (e.g., a FIFOsignal history, a retained test outcome, etc.). The signal history 2136may be employed within a testing block 2137 so as to perform a test overa substantial dataset, average test results over a dataset, etc. Inaspects, the testing block 2137 may be configured to accept or interactwith one or more characteristics 2141 obtained and/or stored along withthe models in the model bank 2139. In aspects, the signal history 2136may be offloaded from a device (e.g., offloaded from a phone to adatacenter), where one or more tests may be performed and an updatedmodel may be downloaded to the device (e.g., from a datacenter to aphone). Such an implementation may be advantageous for leveraging thecomputational resources of a datacenter, and/or signal histories andtest results from a plurality of related devices (e.g., potentially froman entire device population), in assessing an estimator 2140 update,without relying heavily on device resources. In aspects, the testingblock 2137 may be configured to calculate one or more parameters orcharacteristics 2138 (e.g., a measured characteristic) for comparisonagainst one or more models in the model bank 2139. A resulting model,filter coefficients, weighting function, etc. may then be loaded intothe estimator 2140, based upon this comparison as part of an updating oradaptation process thereupon.

FIG. 21e shows aspects of a testing and loading a function,coefficients, weights, etc. into an estimator in accordance with thepresent disclosure. The testing function 2160 may be configured toaccept an input history, a feedback signal history, etc., and one ormore characteristics, coefficients, and/or features 2157 from one ormore models in a model class 2153, and to calculate one or morecharacteristics for comparison against a class of models 2153. Thetesting function 2160 may determine a suitable model, weights, etc. forestimating one or more speaker states for an individual device, group ofdevices, etc. and may load a model, a sub-class of models, etc. onto thedevice, or group of devices, each including an estimator in accordancewith the present disclosure. In aspects, such a testing function 2160may output a group of models, features, characteristics, weightingfunctions, etc. for uploading 2165 into a model bank 2170 (e.g., locatedon a device, in a cloud, attached to a user profile, etc.). In aspects,the estimator may be configured to accept one or more parameters 2175(e.g., model parameters, filter coefficients, etc.), which may be loadedinto the estimator from the model bank 2170. The model bank 2170 mayinclude a plurality of models (e.g., parametric model parameters, filtercoefficients, etc.) representative of the device in question andoptionally one or more model characteristics (e.g., impedanceparameters, resonant frequency, acoustic quality, frequency responseplots, biquad filter coefficients, weighting functions, etc.), which maybe used to determine which model most closely fits a test measurementwithout requiring significant computational load.

In aspects, the estimator may run in parallel with any testing function2160, etc. The loading process 2165 may be configured to include atransitional period whereby the updated model and/or weights are slowlyintroduced so as to minimize the chance of audible transitions, overexcursion events, etc. during the estimator update.

In aspects, one or more components of the testing and/or updatingprocedure may be offloaded from the device 2150, 2155. In aspects, thetesting and/or updating procedures may be performed in a data center, ona server, a cloud service, etc. In aspects, the testing procedure may bevirtualized in accordance with the present disclosure (e.g., enhancedthrough additional statistical modeling, tolerance variation testing,cross population testing, testing within product manufacturing groupTDs, etc.).

The loading process may be initiated by a test in accordance with thepresent disclosure. In aspects, such a test may be performed on thedevice (e.g., in combination with one or more forms of feedback).

In aspects, the testing procedure may be part of a quality controlsystem in accordance with the present disclosure. The quality controlsystem may be configured to periodically collect signal histories fromdevices in the field (e.g., post sales) and generate one or morecharacteristics therefrom. Some non-limiting examples of suchcharacteristics include speaker impedance, acoustic quality, resonantfrequencies, impedance on resonance, thermal-impedance relationships,compliance, property trends, usage history, event logs, environmentalhistory, kinetic history (e.g., movement/impact history of the device),etc. Such information may be used to update lifetime models specific toa particular device (e.g., due to a combination of usage scenario,measured properties, environmental history, etc.).

In aspects, such models may be used to predict lifetime of a particulardevice. In particular, such models may be used to update the estimatorand/or protection features of a particular device in order to extend theservice lifetime thereof. Such changes may include increasing theclamping effects on a speaker associated with a particular device, so asto extend the lifetime thereof, uploading a compressor model thereto,altering an event functional characteristic, updating an estimator, etc.

In aspects, such a quality control system may be valuable in updatingfamilies of device, reducing returns, improving customer satisfaction,catching potential problems before they arise, debugging field relatedfailures, assisting with next generation device design, etc.

FIGS. 22a-c show aspects of a speaker protection system and/or a controlsystem each in accordance with the present disclosure. FIG. 22a showsaspects of a feedback block 2220 (e.g., which may be included within atesting block in accordance with the present disclosure, etc.). Thefeedback block 2220 may be configured to accept one or more feedbackparameters for use in an associated estimator 2210, protection block,testing function, a proximity state evaluator, a proximity modelconstructor, etc. Some non-limiting examples of feedback signals includecurrent, speaker charge, voltage 2204, transducer movement 2206 (e.g.,measured excursion, estimated from a light-based sensor, a capacitivesensor, velocity, acceleration thereof, etc.), a kinetic and/orkinematic feedback signal 2205 (e.g., an impact signal, one or moremovement variables associated with the host CED, etc.), an image of anearby object, an infrared feedback signal, feedback from a microphonein accordance with the present disclosure, an orientation signal, analtitude, an environmental signal, a humidity signal, etc. Such feedbackmay be used alone or in combination to generate a characteristic forcomparing precision of fit for a group of models (e.g., an impedancemeasurement, a near DC resistance measurement, a temperature estimate,an impedance parameter, a resonant frequency, quality factor, bandwidth,a proximity model, a proximity parameter, a proximity state, etc.). Suchcharacteristics may be used within a model selector 2225 to weight,load, and/or adapt 2230 one or more estimation models so as to best fitthe present speaker configuration in question. An associated estimator2210 in accordance with the present disclosure may run in parallel withthe feedback and model selection process, configured to accept an input2201 and produce an output 2215 associated with the present, future, orblock of state values associated with the speaker in question. Inaspects, the estimator 2210 may be configured to provide a powerestimate/predictor 2232 in accordance with the present disclosure.

In aspects, the group of models may generate estimates of the feedbacksignals from the input signals 2201, and the model selector 2225 maycompare the estimates against the feedback signals 2204 for purposes ofselecting the associated model to run within the estimator 2210. Inaspects, a current measurement may be used as the feedback signal 2204,the group of models may be a group of current-estimating models, eachconfigured to generate a feed-forward estimate of speaker current withina characteristic frequency band from the input signal 2201. Theestimated currents may be compared with the measured current todetermine which model in the group is most accurate over any given timeperiod. The model selector 2225 may select the excursion modelassociated with the most accurate current-estimating model for use inthe estimator 2610 as part of the speaker protection system. In aspects,the model selector 2225 may be configured to generate a weightingfunction or interpolation function across multiple models, for usewithin the estimator 2210 (e.g., so as to best fit an excursion estimatefrom a plurality of parallel running excursion models).

In aspects, the estimator 2210 may include a plurality of feed forwardmodels, each predicting an output signal 2215 associated with the input2201. In aspects, the model selector may be configured to compareestimator 2210 values, compare feedback predictions 2220 against thefeed forward models, etc. in order to weight, select, enable, and/ormodify the models so as to provide a sufficiently representative outputsignal 2215 while preserving computational power, relaxing real-timefeedback requirements, and minimizing hardware requirements for thesystem.

In aspects, the model selector 2225 may be configured to accept one ormore performance limitation criteria (e.g., a thermal model, an acousticcoupling, an acoustic power level near the speaker, a relationshipbetween background noise between a nearby sensor and a remote sensor, anexcursion limitation, a power consumption limitation of the associateddevice [e.g., a configurable criteria], a power constraint deliveredfrom a power manager, etc.) for use in the selection process,determining a model fit, etc.

FIG. 22b shows aspects of a speaker protection system and/or a controlsystem in accordance with the present disclosure. The speaker protectionand/or control system system includes a characteristic extraction block2245, configured to derive one or more measured characteristics 2247from one or more feedback signals 2204 each in accordance with thepresent disclosure. The extraction process may be periodic (e.g.,updated every few seconds, minutes, days, etc.), or slowly varyingfunction updated from a continuous stream of data. In aspects, theextraction process may be performed in an OS setting with unreliablelatency (e.g., a non-RT OS setting).

In aspects, the characteristic extraction block 2245 may include acollection of bandpass or notch filters, each filter may be configuredso as to assess a signal 2204 over a limited bandwidth. Output from thecollection of filters may be representative of the frequency content offeedback signal, or of generated signals (e.g., an impedance signal). Inaspects, the output from the collection of filters may be configured soas to determine a frequency associated with a resonant peak in theimpedance spectrum of the impedance signal. Such a determination may bemade by comparing the low pass filtered absolute values (or squares) ofthe outputs from the collection of filters. Such a configuration may besuitable for extracting a characteristic (e.g., a characteristicfrequency of the impedance of the device), in pseudo real-time withoutsignificant computational resources.

In aspects, the characteristic may be used as part of a look upprocedure, comparison, weighting algorithm, etc. in order to select,enable, update, and/or calculate model or filter coefficients,parameters, or the like to be loaded 2257, 2259 into an estimator 2240in accordance with the present disclosure. An associated estimator 2240in accordance with the present disclosure may run in parallel with thefeedback and model selection process, configured to accept an input 2201and produce an output 2215 b associated with the present, future, orblock of state values associated with the speaker in question. Inaspects, the estimator 2240 may be configured to provide a powerestimate/predictor 2262 in accordance with the present disclosure.

In aspects, the group of models included in the model bank 2250 may beconfigured to generate estimates of the feedback signals and/orcharacteristics from the input signals 2201, and a comparison betweenthe estimates and the feedback be used to select which associated stateestimating models may be loaded and/or configured to run within theestimator 2240.

In aspects, a current and voltage measurements may be used as thefeedback signal 2204, the group of models may be a group ofcurrent-estimating models, each configured to generate a feed-forwardestimate of speaker current within a characteristic frequency band fromthe input signal 2201 and each associated with an excursion model, whichcan be loaded and/or enabled to run within the estimator. The estimatedcurrents may be compared with the measured current to determine whichmodel in the group is most accurate over any given time period. Theexcursion model associated with the best fit current-model may be loaded2257, 2259 into the estimator 2240 as part of the speaker protectionsystem. A load/alert block 2255 may be configured to overview thetransition process, weight the incoming and outgoing models in order tosmooth the model transition, etc.

FIG. 22c shows aspects of a speaker protection system in accordance withthe present disclosure. The speaker protection system includes a look uptable based comparison between a measured characteristic 2276 andcharacteristics 2277 associated with a model bank 2285 in accordancewith the present disclosure. In aspects, the characteristics 2277 may bestored in a characteristic LUT 2280 associated with the models in themodel bank 2285. The LUT 2280 may be used to determine which model toload 2290 in to an associated estimator 2270 in accordance with thepresent disclosure. An associated estimator 2270 in accordance with thepresent disclosure may run in parallel with the feedback and modelselection process, configured to accept an input 2201 and produce anoutput 2215 c associated with the present, future, or block of statevalues associated with the speaker in question. In aspects, theestimator 2240 may be configured to provide a power estimate/predictor2296 in accordance with the present disclosure. The measuredcharacteristic(s) 2276 may be generated via a characteristic extractionblock 2275, and one or more feedback signals 2204 each in accordancewith the present disclosure.

FIGS. 23a-c show aspects of a speaker protection system and/or a controlsystem each in accordance with the present disclosure. FIG. 23a showsaspects of a compressor function 2310 included in a protection block inaccordance with the present disclosure. The compressor function 2310 maybe configured to accept a signal 2301 (e.g., an input signal or a signalgenerated therefrom) and an estimating signal 2315. In aspects, one ormore functional relationships within the compressor function (e.g., suchas gain, rails, compression falloff, etc.), may be dependent upon theestimating signal 2315. In aspects, the gain may be set to apredetermined value for estimating signals 2315 of less than a thresholdvalue. When the estimating signal increases beyond the threshold value,the gain may be decreased so as to clamp the output 2302 of thecompressor function in a single or multi-band compressor/limiterstructure. In aspects, the compressor function may be used to adjustand/or throttle the acoustic output of the speaker dependent upon aproximity state, an acoustic coupling to the local acoustic environment,etc.

FIG. 23b shows aspects of a compressor function 2320 included in aprotection block in accordance with the present disclosure. Thecompressor function 2320 may be configured to accept a signal 2301(e.g., an input signal or a signal generated therefrom), an estimatingsignal 2325, a kinetic and/or kinematic feedback signal 2330, and/or anadditional form of feedback (e.g., usage history, environmental feedbacksignal, a proximity state, a proximity model, etc.) each in accordancewith the present disclosure. One or more functional relationships withinthe compressor function 2320 (e.g., such as gain, limits, fall off,knees, etc.), may be dependent upon one or more of the estimating signal2325, the feedback signals 2330, etc. In aspects, the kinetic feedbacksignal 2330 may include an event driven interrupt (e.g., a binary signalrelating to an event such as free fall, an impact, a maximum rotationrate, a rapid change in ambient conditions, a rapid change in altitude,etc.) suitable for transitioning one or more properties of thecompressor function 2320 so as to limit the output 2302 b therefrom,during and/or for a period following such an event. Such animplementation may be advantageous for limiting development of spuriousmodes (e.g., rocking modes, etc.) that may occur in an associatedspeaker during a combination of a kinetic event and large excursion.

FIG. 23c shows aspects of a time history of a kinematic feedback signal2350 and a compressor output of an audio stream 2340 (envelop shown forclarity). The kinematic feedback signal 2370 indicates an impact eventat time t₀ 2356. Upon receipt of the signal, the compressor functionrapidly clamps the audio output thereof (e.g., reduces the envelope froma normal operating amplitude 2342, to a safe operating amplitude 2344)and slowly recovers the gain back to a preconfigured value 2346. Such aconfiguration may be advantageous in helping a speaker to survive animpact event, preventing a speaker from entering into a rocking modeduring and/or immediately after an impact event, etc.

In aspects, the system may include a multi-band compressor structurewith slow release (so as to minimize the pumping effect on the sound).An excursion estimating function and/or limiter may be focused on anexcursion prone band (e.g., up to 1 kHz, 2 kHz, 4 kHz, etc.). Such aconfiguration may be advantageous for allowing the multi-band structureto work more aggressively while the excursion limiter less so and withless aggressively changing the audio signature while providingacceptable safety limits and/or an acoustic coupling between the speakerand a nearby object.

In aspects, the excursion limiter in the protection block may beconfigured with a very short release-time (e.g., essentially asoft-clipping of the excursion peaks).

FIGS. 24a-b show aspects a model selection process in accordance withthe present disclosure. FIG. 24a shows a time series of a measuredcharacteristic 2410 (e.g., such as a characteristic frequency, anon-linearity, a distortion parameter, etc.) over a long period of time,for multiple devices. As can be seen in FIG. 24a , early in the life ofthe devices 2425, both characteristics follow similar aging trajectory.At some point in time in the field, one device 2415 experiences an event2420 (e.g., a device failure event, an impact, etc.) and thecharacteristic trajectories diverge. One or more test procedures inaccordance with the present disclosure may be configured to detect suchan event 2420 and report the event to a quality service, issue a devicespecific update (e.g., reduce speaker output so as to prevent furtherdamage), initiate a repair request, alter an associated speakerprotection algorithm, clamp audio output to the speaker to preserveremaining service life, etc.

FIG. 24b shows aspects of a model selection process in accordance withthe present disclosure. A model bank 2435 including models associatedwith normal operation, with operation that is known to lead to eventualfailure, and/or with models associated with known failure modes are madeavailable for reference to measured characteristics obtained frommeasured feedback signal(s) 2404. The measured characteristics 2430 maybe compared against aspects of the model bank 2435 to determine asuitable model to load 2440 into an estimator in accordance with thepresent disclosure. The comparison may further be used to determine oneor more states of the device (e.g., normal operation, progressingtowards failure, failed, proximity to a nearby object, acoustic couplingto an ear), etc. Such comparison may be used to signal 2450 anassociated alert system 2455 in order to issue a repair statement,identify a recall candidate, indicate a stress event has occurred,initiate changes to a lifetime estimation algorithm, send a message to auser, etc.

In aspects, an estimator, a compressor, or an adaptive control system inaccordance with the present disclosure interacting therewith may includea control strategy based upon one or more of adaptive control,hierarchical control, neural networks, Bayesian probability,backstepping, Lyapunov redesign, H-infinity, deadbeat control,fractional-order control, model predictive control, nonlinear damping,state space control, fuzzy logic, machine learning, evolutionarycomputation, genetic algorithms, optimal control, model predictivecontrol, linear quadratic control, robust control processes, stochasticcontrol, combinations thereof, and the like. In aspects, the estimator,compressor, or adaptive controller may include a full non-linear controlstrategy (e.g., a sliding mode, bang-bang, BIBO strategy, etc.), as alinear control strategy, or a combination thereof.

In aspects, the estimation and/or compression process may be configuredin a fully feed-forward approach (e.g., as an exact input-outputlinearization controller, a linear filter, a linear phase filter, aminimum-phase filter, a set of bi-quad filters, etc.). Alternatively,additionally or in combination, one or more aspects of the estimatorand/or compressor may include a feed-back controller (e.g., a nonlinearfeedback controller, a linear feedback controller, a PID controller,etc.), a feed-forward controller, combinations thereof, or the like.

In aspects, one or more of the feedback signals may be obtained from oneor more aspects of an associated audio system. Some non-limitingexamples of feedback signals include one or more temperaturemeasurements, impedance, charge, coulomb counting, drive current, drivevoltage, drive power, one or more speaker-related kinematic measurements(e.g., membrane or coil displacement, velocity, acceleration, air flow,etc.), sound pressure level measurement, local microphone feedback,ambient condition feedback (e.g., temperature, pressure, humidity,etc.), kinetic measurements (e.g., force at a mount, impact measurement,etc.), B-field measurement, local acoustic impedance, acoustic load onthe speaker, combinations thereof, and the like.

The states may be generally determined as input to the protection blockor a control block. In aspects, one or more states may be transformed soas to reduce computational requirements and/or simplify calculation ofone or more aspects of the system.

In general, the fundamental mode of the speaker cone (e.g., thefundamental resonant frequency), may be determined by using a chirpsignal that starts as a low frequency sine wave and increases thefrequency with time until it reaches a desired end frequency. Theimpedance may be calculated by capturing the driver output current and(optionally) voltage during such testing. An approximate function of thespeaker coil impedance may be acquired by linearization around theequilibrium point. The approximation may be valid for small signalsrelating to small cone excursions. By using that, it may be possible tomatch a measured impedance curve to it to calculate adequate startingspeaker parameters.

In aspects, a control system or speaker protection system in accordancewith the present disclosure may be configured to calculate a powerdelivery value during use thereof. The power delivery value may be anearly indicator of an impending thermal spike and/or excursion. Inaspects, a control system in accordance with the present disclosure maybe configured to accept the power delivery value and to utilize thepower delivery value in one or more control algorithms (e.g., as part ofa compressor, as part of a distortion correction algorithm etc.), one ormore models (e.g., an observer, an excursion prediction algorithm,etc.), and/or one or more speaker protection algorithms (e.g., as atransient load predictor, in combination with one or more temperaturemeasurements, etc.). In aspects, the power delivery value may be used incombination with one or more temperature and/or impedance readings inorder to provide an early alert algorithm to avoid damage (thermal,mechanical, etc.) of the speaker during use. In one non-limitingexample, a control system in accordance with the present disclosure maybe configured to limit the output signal to an associated speaker inaccordance to the power delivery value (e.g., the overall powerconsumption of the speaker, the time averaged power consumption of thespeaker, the spectrally modified power consumption of the speaker,etc.).

In aspects, a control system and/or speaker protection system inaccordance with the present disclosure, may be configured to forecast alifetime (e.g., an overall expected lifetime, a remaining lifetime, orthe like) for a speaker during use. The lifetime forecast may beconfigured to accept one or more stress indicators (e.g., temperature,excursion, power consumption, environmental stresses [e.g., ambienttemperature, humidity, etc.], accelerations [e.g., drop stresses, etc.],combinations thereof, and the like) during use. In aspects, a forecastmay be formed in part by creating and/or accepting one or moretimestamps (e.g., an initial startup date, a warranty date, the presentdate, total on-time to date, he minimum allowable run time of thespeaker until expiration of a warranty, etc.) associated with the use ofthe speaker.

In aspects the forecast may be configured to calculate a stress-timeaccumulator associated with the history of the usage of the speaker to apresent point in time. In one non-limiting example, a stress-timeaccumulator may be calculated by integrating (e.g., leaky integrating,accumulating, etc.) a stress function over time so as to generate anincreasing numerical value. In aspects, the stress function may bedependent on the associated speaker family, and/or may be generated fromone or more lifetime tests performed on a given family of speakers(e.g., a function created during one or more lifetime tests thereof, afunction created from one or more accelerated lifetime tests duringproduct development/manufacturing/field testing, or the like, one ormore field recall assessments [e.g., field based reports on stress-timeaccumulation to failure from a related product population, etc.]). Inaspects, the present stress-time accumulator may be assessed at any timeduring the usage of the device for use in the lifetime prediction (e.g.,as part of a method and/or system to determine the remaining lifethereof).

In aspects, the stress-time accumulator may be a measure of the usageseverity of the associated speaker over the lifetime thereof. In makinga prediction of the remaining lifetime, one or more aspects of thesystem may compare one or more time stamps with the stress-timeaccumulator, one or more stress functions, and/or one or more lifetimetests to generate a lifetime ratio of the usage to date versus a maximalusage to failure.

In aspects, the maximal usage to failure may be determined based on oneor more speaker family accelerated lifetime tests, field recall data,etc. The maximal usage to failure may include one or more safety factorsto ensure that an acceptable percentage of the speaker family wouldsurvive until such a level during use (e.g., 96% of all speakers in thefamily, 99% of the speakers, etc.).

Thus, the ratio may be used to predict remaining lifetime of thespeaker, based upon the stress-time accumulator at a present moment intime.

In aspects, the lifetime ratio may be compared with one or moretimestamps in order to predict how much time may be left to failure ofthe associated speaker. In aspects, the ratio may be used as a controland/or protection parameter to limit the maximal stress that a speakermay be put under during future usage, in order to extend the minimalexpected lifetime thereof beyond a predetermined point in the future(e.g., until after a warranty expiration, until a predetermined timefrom purchase, until a predetermined maximal usage, etc.).

By way of non-limiting example, a first customer may heavily use aspeaker in accordance with the present disclosure when the speaker isfirst put into service. Based upon the stress-time accumulator, aspeaker protection algorithm in accordance with the present disclosuremay limit the maximal stress levels that the first customer can continueto place the speaker under going forward, so as to extend the lifetimethereof to beyond a timestamp in the future. By way of non-limitingexample, a second user may intermittently use a speaker in accordancewith the present disclosure at high stress levels but only over shortperiods at a time up until a present time period. Based upon thestress-time accumulator after a given period of time, a forecast may bemade to determine that the usage profile for the second customer mayresult in an adequately long lifetime for the associated speaker, thus aspeaker protection algorithm in accordance with the present disclosure,may leave the maximal stress levels at the factory settings.

A forecast in accordance with the present disclosure, may be used incombination with one or more long term lifetime planning algorithms(e.g., so as to manage the lifetime of a component, a speaker, etc.), aspart of a service contract dispute (e.g., so as to determine if theusage profile of a customer was within a contractual limit), as part ofa diagnostic and/or forensic test (e.g., to determine when/why a speakerfailed in service), combinations thereof, and the like.

In aspects, the forecast may be used as part of a usage profilecalculation (e.g., so as to characterize the usage profile of acustomer). The usage profile may be used to calculate one or morefatigue related damage accumulation, fatigue life calculations,temperature and excursion limits, combinations thereof, and the like.The usage profiles may then be used to limit speaker response, only ifthe over-use thereof is expected to lead to a diminution of the lifetimethereof within a warranty period, etc.

In aspects, the absolute maximums in addition to the dynamic aspectsthat look at a ratio of dwell time and power/temperature levels toensure speaker safety.

In aspects, an additional observer may be configured to predict theexcursion of the speaker from a combination of the input signals andfeedback signals derived from the speaker and/or sensory feedback blocksin accordance with the present disclosure. Such a configuration may beadvantageous for predicting excursion issues before they arise inpractice, so as to clamp down on the drive signals before an excursionlimit is hit (thereby avoiding damage to the associated speaker).

In aspects, the resonant frequency of a speaker may be mapped to thespectral impedance curve of an associated speaker in accordance with thepresent disclosure. By design an adaptive filter following the resonantpeak based on the impedance curve, the resonant peak of the speaker canbe suppressed. The resulting system may be advantageous for protecting aspeaker with a behavioral model that is consistent for one or moreaspects of frequencies, over changing temperature, aging fatigue etc.

In aspects, methods for recalculating these curves (and thetemperature/amplitude dependence thereof in the field) may beadvantageous to cover changes to models caused by damage to anassociated speaker in the field, changes in climate (e.g., danderbuildup on the speakers themselves, changes in local humidity, etc.).

Methods for simultaneous prediction of temperature and excursion duringuse of a speaker element may be envisaged as depicted throughout thepresent disclosure. Methods may be envisaged to calculate the changingimpedance curve with natural music, other approaches, etc.

In aspects, the system may include an observer configured to combineresistance/impedance measurements with some predictive algorithms basedon temperature behavior models so as to look at an input signal inadvance (e.g., a delayed version may be sent through to the speaker andan immediate version through the observer), and “see” that it will leadto rapid heating, and/or excursion. Such a configuration may beadvantageous for predicting when a thermal and/or excursion stress onthe speaker may be sufficiently dangerous, so as to avoid damage to thespeaker during use.

In aspects, one or more methods for obtaining excursion from impedancespectra may be coupled with temperature readings as the curves maychange with excursion (due to nonlinearities) and temperature (due totemperature related property changes of speaker components).

In aspects, the method may include watching the excursion of the speakerso as to predict imminent failure thereof and rapidly clamping down onthe input to the speaker in order to prevent such failure.

In aspects, an algorithm may be provided for predicting temperature andexcursion in real-time to protect against immediate failure and toprotect against longer term failure due to excessive use of the speakerat significant stresses that are below the immediate failure concerns(yet equally dangerous over the long term).

Thermal aspects may be regulated based on actual temperature limits ofthe elements involved while excursions may be limited based on a currentreading (e.g., an observer is run in parallel with the actual path). Inthis sense, the actual path may be slightly delayed with respect to theobserver. In aspects, if a dangerous excursion is predicted by theobserver, the actual path becomes clamped so as to prevent damage to thespeaker

In aspects, an active speaker in accordance with the present disclosuremay include one or more onboard sensors for temperature, humidity,and/or excursion, combinations thereof, or the like. In aspects,excursion may be measured based on magnetic field measurementimmediately beside the speaker coil. In aspects, excursion may bemeasured based on optical sensor placed into a SiP integrated speakerdriver. In aspects, the sensory feedback may be made available to one ormore aspects of the system (e.g., a nonlinear controller, a controller,a protection circuit, etc.). In aspects, excursion may be estimatedbased on back cavity pressure measurement (e.g., MEMS pressure sensorintegrated into the SiP). In aspects, such sensors may dual asaltimeters/barometers for other functions of the phone, which couldresult in cost savings by coupling with the speaker package instead ofas a stand-alone chipset.

In aspects, the integrated circuit may be embedded into the speakeritself, the integrated circuit may be configured so as to measure one ormore impedance values during use. Such a configuration may beadvantageous for measuring values without having to past through aconnector (as would be required with an off-speaker chipset).

In aspects, an active speaker may allow for a reduction in contactresistance fluctuations seen in connector impedance during use, underlifetime considerations, etc. In aspects, the active speaker may includea power control system in order to adapt the power rails if necessaryduring operation (e.g., so as to increase the overall power that may beprovided to the speaker during use, so as to compensate for impedance ofa connector between the power supply and the active speaker, etc.).

In aspects, the active speaker may be coupled into a PCB via a snap-inconnector. Such a configuration may be advantageous to provide acombination of easy assembly with improved performance (e.g., toovercome contact impedance variation of such connectors amongst aproduct population). Such a configuration may be advantageous forproviding a high performance speaker with a simple non-solderedconnectors used for micro-speakers in mobile applications.

An active speaker in accordance with the present disclosure may beconfigured to communicate with one or more aspects of an associatedsystem through means of a communication bus. Such a configuration mayallow for simplified operation (e.g., power plus a digital signal may beprovided by a processor), also digital communication may allow forhigher levels of system awareness and diagnostics (e.g., by providingtwo-way communication between speaker and source). Such a configurationmay allow for programming of speaker parameters, communication ofspeaker parameters (either factory programmed, or obtained from internalassessments, etc.), feed-back of sensor readings to the host etc.

In aspects, a system in accordance with the present disclosure mayinclude an audio impending power requirements prediction in accordancewith the present disclosure. Such a power prediction may be performed ina similar manner to the excursion prediction (e.g., in parallel with it,on a block by block basis, etc.), the results of which could be madeavailable to a system power manager, compared against a powerconstraint, or the like. Such a configuration may be advantageous forfeeding a power management system with upcoming resource requirementsfor the speaker.

In aspects, the audio control system may be configured to accept a powerconstraint from an external power manager (e.g., from somewhere else inthe system). The corresponding protection block/compressor, etc. may berailed or limited so as to further constrain operation based upon thepower constraint (e.g., to work within the confines of what the systemannounces that it can provide to the audio system).

In aspects, the power constraint may be coupled with an implied medianetwork application, to automatically throttle audio output when devicesenter into “quiet zones” such as theaters, hospitals, or the like. Insuch applications, the power constraint may be set when a deviceregisters with a local wireless network, joins a network group, obtainsa network ID, or the like.

Thus the passage of power predictions and/or power constraints may beused by a system to manage “soft” power transitions, due to events, thusforming a a “responsible” audio system that can manage operation underconstrained power as well as report back near future power requirementsto a system controller.

In aspects of the present disclosure, the term block computation ismeant to include, without limitation, simultaneous computation of atemporal block of samples computed in a manner suitable, for purposes ofintegrating with a software host, for use within an operating systemcallback structure, to alleviate the time-sensitive nature ofcalculations, and/or to relieve the “always on” aspects of asample-to-sample feedback controlled system. Such a configuration may beamendable to operation in a non-real-time operating system, such as amobile operating system (e.g., iOS, Android, Windows 8, or the like).

It will be appreciated that additional advantages and modifications willreadily occur to those skilled in the art. Therefore, the disclosurespresented herein and broader aspects thereof are not limited to thespecific details and representative embodiments shown and describedherein. Accordingly, many modifications, equivalents, and improvementsmay be included without departing from the spirit or scope of thegeneral inventive concept as defined by the appended claims and theirequivalents.

1.-42. (canceled)
 43. A system for determining a proximity staterelating to a proximity of a speaker from an ear of a user, the systemcomprising: an audio path configured to deliver an audio signalcomprising a diagnostic signal to the speaker; and proximity estimatorcircuitry configured to: receive a feedback signal from a feedbackmicrophone configured to monitor the output of the speaker in thevicinity of the speaker, wherein the feedback signal is based on thediagnostic signal; and analyze the diagnostic signal and the feedbacksignal to calculate the proximity state.
 44. The system of claim 43wherein the proximity state comprises one of: a free state, wherein thespeaker is far from the ear of the user; and a restricted state, whereinthe speaker is coupled with the ear of the user so as to form asubstantially complete gasket.
 45. The system of claim 43 wherein theproximity estimator circuitry is configured to: generate an estimate ofthe feedback signal from the audio signal; and compare the estimate ofthe feedback signal against the feedback signal to calculate theproximity state.
 46. The system of claim 43 wherein the proximityestimator circuitry is configured to: derive a measured characteristicfrom the feedback signal; and compare the measured characteristic withone or more model characteristics to calculate the proximity state. 47.The system of claim 43 wherein the diagnostic signal comprises anacoustic watermark.
 48. The system of claim 43 further comprising thefeedback microphone, wherein the feedback microphone is locatedsubstantially between the speaker and the object.
 49. The system ofclaim 48 wherein the feedback microphone comprises a wirelessmicrophone.
 50. The system of claim 43 wherein the proximity estimatorcircuitry is configured to assess a coupling between the speaker and thefeedback microphone using a feedback algorithm.
 51. The system of claim50 wherein feedback algorithm updates the proximity state slower than arendering rate.
 52. The system of claim 43 wherein the system isconfigured to input the feedback signal into a parametric extractionalgorithm to generate a control parameter related to a degree of agasket between the speaker and the ear of the user.
 53. The system ofclaim 43 wherein the proximity state is reflective of one or more of: anacoustic coupling, acoustic leakage, acoustic volume and tightness offit between the speaker and the ear of the user.
 54. The system of claim43 wherein the proximity estimator circuitry is configured to calculatethe proximity state using changes in background noise level.
 55. Thesystem of claim 54 wherein the proximity estimator circuitry isconfigured to calculate the proximity state by comparing the backgroundnoise level in two locations.
 56. The system of claim 43 wherein thesystem is configured to alter the audio signal based on the proximitystate.
 57. The system of claim 56 wherein the system is configured toclamp the audio signal or restore the audio signal based on whether theproximity state indicates establishment or loss of a gasket between thespeaker and the ear of the user.
 58. The system of claim 43 wherein theproximity state is calculated in real time or pseudo real time.
 59. Thesystem of claim 43 wherein the system is configured to output aninterrupt flag based on whether the proximity state indicatesestablishment or loss of a gasket between the speaker and the ear of theuser.
 60. The system of claim 43 wherein the proximity estimatorcircuitry is configured to use frequency sensitivity measurements todetect an extent of a gasket between the speaker and an ear of the user.61. The system of claim 43 wherein the proximity estimator circuitry isconfigured to calculate the proximity state based on a change in powerspectra indicative of an extent of an ear gasket formed.
 62. The systemof claim 43 wherein the proximity estimator circuitry is configured touse multiple frequency bands to calculate the proximity state.
 63. Thesystem of claim 43 wherein proximity estimator circuitry is configuredto use power or energy calculations on the feedback signal, limited byfrequency band, to calculate the proximity state.
 64. A consumerelectronic device comprising a system as claimed in claim
 43. 65. Theconsumer electronic device of claim 64 comprising a media accessory. 66.The consumer electronic device of claim 65 comprising a phone or tabletaudio and/or video accessory.
 67. An audio system for generating asignal indicative of the proximity of a speaker from an ear of a user,the audio system comprising: an input for receiving one or more inputsignals, wherein at least one of the input signals comprises adiagnostic signal; an output for outputting an output signal to thespeaker, wherein the output signal is based on the input signals; and anestimator for receiving, from a microphone located facing the speaker, afeedback signal based on the output signal, wherein the estimatorgenerates the signal indicative of the proximity of the speaker from anear of a user based on the diagnostic signal.
 68. A method fordetermining a proximity state relating to a proximity or distance of aspeaker from an ear of a user, the method comprising: outputting to thespeaker an audio signal comprising a diagnostic signal; monitoring theoutput of the speaker in the vicinity of the speaker to generate afeedback signal based on the diagnostic signal; and analyzing thediagnostic signal and the feedback signal to calculate the proximitystate.
 69. The method of claim 68 further comprising: clamping the audiosignal or restoring the audio signal based on whether the proximitystate indicates establishment or loss of a gasket between the speakerand the ear of the user.
 70. The method of claim 68 further comprising:assessing a coupling between the speaker and the feedback microphoneusing a feedback algorithm.
 71. The method of claim 68 furthercomprising: inputting the feedback signal into a parametric extractionalgorithm to generate a control parameter related to a degree of agasket between the speaker and the ear of the user.